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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| Format: | Article |
| Language: | English |
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De Gruyter
2025-02-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2024-0167 |
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| author | Elhoseny Mohamed Hassan Ahmed Shehata Marwa H. Kayed Mohammed |
| author_facet | Elhoseny Mohamed Hassan Ahmed Shehata Marwa H. Kayed Mohammed |
| author_sort | Elhoseny Mohamed |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-ff96940ec1d948f590c12579988a1bb8 |
| institution | Kabale University |
| issn | 2191-026X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-ff96940ec1d948f590c12579988a1bb82025-02-10T13:24:25ZengDe GruyterJournal of Intelligent Systems2191-026X2025-02-013412194110.1515/jisys-2024-0167Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-timeElhoseny Mohamed0Hassan Ahmed1Shehata Marwa H.2Kayed Mohammed3College of Computing and Informatics, University of Sharjah, Sharjah, 000, United Arab EmiratesFaculty of Science, Beni-Suef University, Beni-Suef, 62511, EgyptFaculty of Science, Beni-Suef University, Beni-Suef, 62511, EgyptFaculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, 62511, EgyptThe 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%.https://doi.org/10.1515/jisys-2024-0167iotsnake bite’s marksegyptian cobradeep learningtransfer learningcloud computing |
| spellingShingle | Elhoseny Mohamed Hassan Ahmed Shehata Marwa H. Kayed Mohammed Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time Journal of Intelligent Systems iot snake bite’s marks egyptian cobra deep learning transfer learning cloud computing |
| title | Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time |
| title_full | Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time |
| title_fullStr | Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time |
| title_full_unstemmed | Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time |
| title_short | Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time |
| title_sort | explainable deep learning approach for recognizing egyptian cobra bite in real time |
| topic | iot snake bite’s marks egyptian cobra deep learning transfer learning cloud computing |
| url | https://doi.org/10.1515/jisys-2024-0167 |
| work_keys_str_mv | AT elhosenymohamed explainabledeeplearningapproachforrecognizingegyptiancobrabiteinrealtime AT hassanahmed explainabledeeplearningapproachforrecognizingegyptiancobrabiteinrealtime AT shehatamarwah explainabledeeplearningapproachforrecognizingegyptiancobrabiteinrealtime AT kayedmohammed explainabledeeplearningapproachforrecognizingegyptiancobrabiteinrealtime |