Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time
The Egyptian cobra is among the deadliest snake species, capable of causing death within a short span of 15 min. Also, every snake species has its own anti-venom type. So, a quick identifying the Egyptian Cobra bite from other snake species is a challenging and critical task. This research employs I...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-02-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2024-0167 |
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| Summary: | The Egyptian cobra is among the deadliest snake species, capable of causing death within a short span of 15 min. Also, every snake species has its own anti-venom type. So, a quick identifying the Egyptian Cobra bite from other snake species is a challenging and critical task. This research employs Internet of things (IoT) and deep learning methods to precisely recognize bites of Egyptian cobra, in the real-time, by analyzing images of the bite marks. We deploy IoT-enabled wearable devices equipped with sensors capable of detecting snake bites, whereas these sensors measure changes in physiological parameters indicative of a snakebite, such as heart rate, blood pressure, and temperature sensors based on our proposed mathematical algorithm. Also, we present a real case study in which we used our mathematical algorithm to determine based on its sensor readings whether the victim was exposed to a snake bite or not in the real-time. These wearable devices can be worn by individuals working or living in areas prone to snake encounters, such as farmers. When a snake bite occurs, the IoT sensors embedded in the wearable devices will immediately detect the bite and transmit real-time data, including vital information about the bite marks, to a central monitoring system or victim relative. Also, we assembled a dataset consisting of 500 images depicting Egyptian cobra bites and 600 images of bites from various other snake species indigenous to Egypt. To bolster the model’s trustworthiness and facilitate understanding of its decisions, we employed the contemporary method of explainable deep learning. Also, notably, our methodology yielded an accuracy of 90.9%. |
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| ISSN: | 2191-026X |