| _version_ | 1866902246332039168 |
|---|---|
| author | V.V.Nagendra Kumar , Venna Jaya Sri |
| author_facet | V.V.Nagendra Kumar , Venna Jaya Sri |
| contents | <p>A huge number of deaths and injuries caused by car accidents is one of the most urgent problems we <br>will face today in the world. Ambulances can be pre-positioned to shorten response time and give immediate <br>medical assistance instead of being dispatched just when needed. When it comes to healthcare decision-making <br>and problem-solving, deep learning approaches have already shown to be indispensable. In order to forecast the <br>best places to position ambulances, this research presents a method based on deep-embedded clustering. It is <br>critical to understand these linkages while creating models because road crash occurrence is heavily influenced <br>by various regional characteristics and patterns. This study uses CAT2VEC, another model of deep learning, to <br>implement the need to maintain such formulas in the entire model building to ensure real -time results. <br>Traditional clustering techniques such as K-Means, GMM and agglomerative clustering are also compared to <br>the proposed framework. For the purpose of evaluating the efficiency of various algorithms, a new feature of <br>scoring to calculate the time and distance of the response in real time has been developed. The proposed <br>ambulance system acts impressively exceeds all other conventional methods, reaches a speed of accuracy of <br>95% with cross validation of K-time and a unique score of 7.581 distance.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15153707 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Geospatial Analysis for Emergency Services: Mapping Accident Hotspots to Enhance Ambulance Accessibility V.V.Nagendra Kumar , Venna Jaya Sri <p>A huge number of deaths and injuries caused by car accidents is one of the most urgent problems we <br>will face today in the world. Ambulances can be pre-positioned to shorten response time and give immediate <br>medical assistance instead of being dispatched just when needed. When it comes to healthcare decision-making <br>and problem-solving, deep learning approaches have already shown to be indispensable. In order to forecast the <br>best places to position ambulances, this research presents a method based on deep-embedded clustering. It is <br>critical to understand these linkages while creating models because road crash occurrence is heavily influenced <br>by various regional characteristics and patterns. This study uses CAT2VEC, another model of deep learning, to <br>implement the need to maintain such formulas in the entire model building to ensure real -time results. <br>Traditional clustering techniques such as K-Means, GMM and agglomerative clustering are also compared to <br>the proposed framework. For the purpose of evaluating the efficiency of various algorithms, a new feature of <br>scoring to calculate the time and distance of the response in real time has been developed. The proposed <br>ambulance system acts impressively exceeds all other conventional methods, reaches a speed of accuracy of <br>95% with cross validation of K-time and a unique score of 7.581 distance.</p> |
| title | Geospatial Analysis for Emergency Services: Mapping Accident Hotspots to Enhance Ambulance Accessibility |
| url | https://doi.org/10.5281/zenodo.15153707 |