Pub Date : 2024-03-20DOI: 10.34104/ajeit.024.037050
This research aims to evaluate the impact of the most recent floods that occurred on August 20, 2022, in Logar province in southern Afghanistan. For this purpose, changes in land use and land cover (LULC) of the study area were created from the Sentinel-2 image with a spatial resolution of 10 meters. To achieve this, the study utilized Sentinel-2 images to analyze LULC changes before and after the flood event and employed a support vector machine for supervised classification. The study also applied the analytical hierarchy process (AHP) to evaluate the future risks of flooding in the study area, focusing on factors related to hydrological phenomena. Overall, the study demonstrates the effectiveness of geospatial technologies and remote sensing in assessing the impacts of floods and creating flood risk maps. This can significantly reduce the consequences of flooding and inform decision-making for disaster management and mitigation.
{"title":"Analyzing Flood Damage and Mapping Flood Hazard Zones Using AHP Model: A Case Study of Pol-e-Alam, Logar Province, Afghanistan","authors":"","doi":"10.34104/ajeit.024.037050","DOIUrl":"https://doi.org/10.34104/ajeit.024.037050","url":null,"abstract":"This research aims to evaluate the impact of the most recent floods that occurred on August 20, 2022, in Logar province in southern Afghanistan. For this purpose, changes in land use and land cover (LULC) of the study area were created from the Sentinel-2 image with a spatial resolution of 10 meters. To achieve this, the study utilized Sentinel-2 images to analyze LULC changes before and after the flood event and employed a support vector machine for supervised classification. The study also applied the analytical hierarchy process (AHP) to evaluate the future risks of flooding in the study area, focusing on factors related to hydrological phenomena. Overall, the study demonstrates the effectiveness of geospatial technologies and remote sensing in assessing the impacts of floods and creating flood risk maps. This can significantly reduce the consequences of flooding and inform decision-making for disaster management and mitigation.","PeriodicalId":505651,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"345 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140228052","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 : 2024-03-09DOI: 10.34104/ajeit.024.026036
One of the main factors that lead to death globally is stroke. The main reason for death by stroke is not taking prevention measures early and not understanding stroke. As a result, death by stroke is thriving all over the world, especially in developing countries like Bangladesh. Steps must be taken to identify strokes as early as possible. In this case, machine learning can be a solution. This study aims to find the appropriate algorithms for machine learning to predict stroke early and accurately and identify the main risk factors for stroke. To perform this work, a real dataset was collected from the Kaggle website and split into two parts: train data and test data, and seven machine learning algorithms such as Random Forest, Decision Tree, K-Nearest Neighbor, Adapting Boosting, Gradient Boosting, Logistic Regression, and Support Vector Machine were applied to that train data. Performance evaluation was calculated based on six performance metrics accuracy, precision, recall, F1-score, ROC curve, and precision-recall curve. To figure out the appropriate algorithm for stroke prediction, the performance for each algorithm was compared, and Random Forest was discovered to be the most effective algorithm with 0.99 accuracy, precision, recall, F1-score, an AUC of 0.9925 for the ROC curve, and an AUC of 0.9874 for the precision-recall curve. Finally, feature importance scores for each algorithm were calculated and ranked in descending order to find out the top risk factors for stroke like ‘age’, ‘average glucose level’, ‘body mass index’, ‘hypertension', and ‘smoking status’. The developed model can be used in different health institutions for stroke prediction with high accuracy.
{"title":"Effective Stroke Prediction using Machine Learning Algorithms","authors":"","doi":"10.34104/ajeit.024.026036","DOIUrl":"https://doi.org/10.34104/ajeit.024.026036","url":null,"abstract":"One of the main factors that lead to death globally is stroke. The main reason for death by stroke is not taking prevention measures early and not understanding stroke. As a result, death by stroke is thriving all over the world, especially in developing countries like Bangladesh. Steps must be taken to identify strokes as early as possible. In this case, machine learning can be a solution. This study aims to find the appropriate algorithms for machine learning to predict stroke early and accurately and identify the main risk factors for stroke. To perform this work, a real dataset was collected from the Kaggle website and split into two parts: train data and test data, and seven machine learning algorithms such as Random Forest, Decision Tree, K-Nearest Neighbor, Adapting Boosting, Gradient Boosting, Logistic Regression, and Support Vector Machine were applied to that train data. Performance evaluation was calculated based on six performance metrics accuracy, precision, recall, F1-score, ROC curve, and precision-recall curve. To figure out the appropriate algorithm for stroke prediction, the performance for each algorithm was compared, and Random Forest was discovered to be the most effective algorithm with 0.99 accuracy, precision, recall, F1-score, an AUC of 0.9925 for the ROC curve, and an AUC of 0.9874 for the precision-recall curve. Finally, feature importance scores for each algorithm were calculated and ranked in descending order to find out the top risk factors for stroke like ‘age’, ‘average glucose level’, ‘body mass index’, ‘hypertension', and ‘smoking status’. The developed model can be used in different health institutions for stroke prediction with high accuracy.","PeriodicalId":505651,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"178 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140256541","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 : 2024-02-14DOI: 10.34104/ajeit.024.019025
ABSTRACT The research aimed to evaluate the field performance of a potato planter powered by a power tiller at the Regional Wheat Research Institute, BARI, Rajshahi. The study was conducted at a farmer's field in the potato-growing region of Shyampur, Rajshahi, from August 2013 to January 2014. The planter maintained a 250 mm gap between seeds and a single row spacing of 60 mm. Field trials were conducted at different operating speeds and seed sizes for assessment. The study revealed that an optimal forward speed of 2.5 km/hr resulted in the most uniform seed spacing and minimal seed gaps. Field demonstrations in Shyampur showed the potato planters' average effective field capacity was 0.11 ha/hr, with a 5% seed absence rate. In comparison to the traditional manual planting method, which required 53.3 man-days/ha, the potato planter significantly reduced labor requirements to 3 man-days per hectare. The total cost of planting was Tk.1781.82/ha. While the conventional method slightly outperformed mechanically planted plots in yields, using the power-tiller-operated potato planter demonstrated significant savings. A farmer's field day showcased crops from both planting methods, highlighting the substantial labor (95%) and cost (53%) savings achieved by adopting the mechanical planting approach. Considering the comparative performance, it is recommended that low-income farmers adopt the power tiller-operated potato planter to increase planting efficiency, cover more area in less time, and significantly reduce production costs compared to traditional methods.
{"title":"Field Performance Evaluation of a Power Tiller Operated Potato Planter","authors":"","doi":"10.34104/ajeit.024.019025","DOIUrl":"https://doi.org/10.34104/ajeit.024.019025","url":null,"abstract":"ABSTRACT\u0000The research aimed to evaluate the field performance of a potato planter powered by a power tiller at the Regional Wheat Research Institute, BARI, Rajshahi. The study was conducted at a farmer's field in the potato-growing region of Shyampur, Rajshahi, from August 2013 to January 2014. The planter maintained a 250 mm gap between seeds and a single row spacing of 60 mm. Field trials were conducted at different operating speeds and seed sizes for assessment. The study revealed that an optimal forward speed of 2.5 km/hr resulted in the most uniform seed spacing and minimal seed gaps. Field demonstrations in Shyampur showed the potato planters' average effective field capacity was 0.11 ha/hr, with a 5% seed absence rate. In comparison to the traditional manual planting method, which required 53.3 man-days/ha, the potato planter significantly reduced labor requirements to 3 man-days per hectare. The total cost of planting was Tk.1781.82/ha. While the conventional method slightly outperformed mechanically planted plots in yields, using the power-tiller-operated potato planter demonstrated significant savings. A farmer's field day showcased crops from both planting methods, highlighting the substantial labor (95%) and cost (53%) savings achieved by adopting the mechanical planting approach. Considering the comparative performance, it is recommended that low-income farmers adopt the power tiller-operated potato planter to increase planting efficiency, cover more area in less time, and significantly reduce production costs compared to traditional methods.","PeriodicalId":505651,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"53 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777495","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 : 2024-02-14DOI: 10.34104/ajeit.024.019025
ABSTRACT The research aimed to evaluate the field performance of a potato planter powered by a power tiller at the Regional Wheat Research Institute, BARI, Rajshahi. The study was conducted at a farmer's field in the potato-growing region of Shyampur, Rajshahi, from August 2013 to January 2014. The planter maintained a 250 mm gap between seeds and a single row spacing of 60 mm. Field trials were conducted at different operating speeds and seed sizes for assessment. The study revealed that an optimal forward speed of 2.5 km/hr resulted in the most uniform seed spacing and minimal seed gaps. Field demonstrations in Shyampur showed the potato planters' average effective field capacity was 0.11 ha/hr, with a 5% seed absence rate. In comparison to the traditional manual planting method, which required 53.3 man-days/ha, the potato planter significantly reduced labor requirements to 3 man-days per hectare. The total cost of planting was Tk.1781.82/ha. While the conventional method slightly outperformed mechanically planted plots in yields, using the power-tiller-operated potato planter demonstrated significant savings. A farmer's field day showcased crops from both planting methods, highlighting the substantial labor (95%) and cost (53%) savings achieved by adopting the mechanical planting approach. Considering the comparative performance, it is recommended that low-income farmers adopt the power tiller-operated potato planter to increase planting efficiency, cover more area in less time, and significantly reduce production costs compared to traditional methods.
{"title":"Field Performance Evaluation of a Power Tiller Operated Potato Planter","authors":"","doi":"10.34104/ajeit.024.019025","DOIUrl":"https://doi.org/10.34104/ajeit.024.019025","url":null,"abstract":"ABSTRACT\u0000The research aimed to evaluate the field performance of a potato planter powered by a power tiller at the Regional Wheat Research Institute, BARI, Rajshahi. The study was conducted at a farmer's field in the potato-growing region of Shyampur, Rajshahi, from August 2013 to January 2014. The planter maintained a 250 mm gap between seeds and a single row spacing of 60 mm. Field trials were conducted at different operating speeds and seed sizes for assessment. The study revealed that an optimal forward speed of 2.5 km/hr resulted in the most uniform seed spacing and minimal seed gaps. Field demonstrations in Shyampur showed the potato planters' average effective field capacity was 0.11 ha/hr, with a 5% seed absence rate. In comparison to the traditional manual planting method, which required 53.3 man-days/ha, the potato planter significantly reduced labor requirements to 3 man-days per hectare. The total cost of planting was Tk.1781.82/ha. While the conventional method slightly outperformed mechanically planted plots in yields, using the power-tiller-operated potato planter demonstrated significant savings. A farmer's field day showcased crops from both planting methods, highlighting the substantial labor (95%) and cost (53%) savings achieved by adopting the mechanical planting approach. Considering the comparative performance, it is recommended that low-income farmers adopt the power tiller-operated potato planter to increase planting efficiency, cover more area in less time, and significantly reduce production costs compared to traditional methods.","PeriodicalId":505651,"journal":{"name":"Australian Journal of Engineering and Innovative Technology","volume":"347 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837079","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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