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S.Siddharth , S.Dheeraj , P.Rakesh, Dr.Pallam Ravi
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Page No: 1 - 8
Abstract : Snake encounters in human-populated and wildlife areas pose significant threats to public safety and biodiversity. Each year, many incidents result in snakebites, often leading to serious injuries or fatalities, and frequently result in harm to snakes due to fear-driven responses. Traditional methods for snake detection, such as visual observation, are typically slow and can lead to delayed or inaccurate responses, increasing the risks associated with human-snake encounters. This study presents a Real-Time Snake Detection and Alert System that employs advanced artificial intelligence (AI) and machine learning (ML) techniques, specifically using YOLO (You Only Look Once), to accurately identify the presence of snakes in pre-recorded videos or static images. Our system processes these visual inputs to determine if a snake is present, providing an immediate, automated alarm to alert individuals nearby if a snake is detected. This system is intended to be a rapid-response solution for detecting snakes in images or video files, enhancing safety in snake-prone areas by increasing awareness of potential threats without requiring constant live monitoring or additional notifications to authorities.
Keyword Snake Detection, Real-Time Alert System, YOLO (You Only Look Once), Machine Learning for Object Detection
Reference:
  1. Lopez, S., Sarada, V., Praveen, R. V. S., Pandey, A., Khuntia, M., & Haralayya, D. B. (2024). Artificial intelligence challenges and role for sustainable education in india: Problems and prospects. Sandeep Lopez, Vani Sarada, RVS Praveen, Anita Pandey, Monalisa Khuntia, Bhadrappa Haralayya (2024) Artificial Intelligence Challenges and Role for Sustainable Education in India: Problems and Prospects. Library Progress International, 44(3), 18261-18271.
  2. Kumar, N., Kurkute, S. L., Kalpana, V., Karuppannan, A., Praveen, R. V. S., & Mishra, S. (2024, August). Modelling and Evaluation of Li-ion Battery Performance Based on the Electric Vehicle Tiled Tests using Kalman Filter-GBDT Approach. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
  3. Sharma, S., Vij, S., Praveen, R. V. S., Srinivasan, S., Yadav, D. K., & VS, R. K. (2024, October). Stress Prediction in Higher Education Students Using Psychometric Assessments and AOA-CNN-XGBoost Models. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1631-1636). IEEE.
  4. Yamuna, V., Praveen, R. V. S., Sathya, R., Dhivva, M., Lidiya, R., & Sowmiya, P. (2024, October). Integrating AI for Improved Brain Tumor Detection and Classification. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1603-1609). IEEE.
  5. Anuprathibha, T., Praveen, R. V. S., Jayanth, H., Sukumar, P., Suganthi, G., & Ravichandran, T. (2024, October). Enhancing Fake Review Detection: A Hierarchical Graph Attention Network Approach Using Text and Ratings. In 2024 Global Conference on Communications and Information Technologies (GCCIT) (pp. 1-5). IEEE.
  6. Praveen, R. V. S. (2024). Data Engineering for Modern Applications. Addition Publishing House.
  7. Dhivya, R., Sagili, S. R., Praveen, R. V. S., VamsiLala, P. N. V., Sangeetha, A., & Suchithra, B. (2024, December). Predictive Modelling of Osteoporosis using Machine Learning Algorithms. In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 997-1002). IEEE.
  8. Kemmannu, P. K., Praveen, R. V. S., Saravanan, B., Amshavalli, M., & Banupriya, V. (2024, December). Enhancing Sustainable Agriculture Through Smart Architecture: An Adaptive Neuro-Fuzzy Inference System with XGBoost Model. In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA) (pp. 724-730). IEEE.
  9. Praveen, R. V. S., Raju, A., Anjana, P., & Shibi, B. (2024, October). IoT and ML for Real-Time Vehicle Accident Detection Using Adaptive Random Forest. In 2024 Global Conference on Communications and Information Technologies (GCCIT) (pp. 1-5). IEEE.
  10. Praveen, R. V. S., Hemavathi, U., Sathya, R., Siddiq, A. A., Sanjay, M. G., & Gowdish, S. (2024, October). AI Powered Plant Identification and Plant Disease Classification System. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1610-1616). IEEE.
  11. Thamilarasi, V., & Roselin, R. (2021, February). Automatic classification and accuracy by deep learning using cnn methods in lung chest X-ray images. In IOP Conference Series: Materials Science and Engineering (Vol. 1055, No. 1, p. 012099). IOP Publishing.
  12. Thamilarasi, V., & Roselin, R. (2019). Lung segmentation in chest X-ray images using Canny with morphology and thresholding techniques. Int. j. adv. innov. res, 6(1), 1-7.
  13. Thamilarasi, V., & Roselin, R. (2019). Automatic thresholding for segmentation in chest X-ray images based on green channel using mean and standard deviation. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(8), 695-699.
  14. Thamilarasi, V., & Roselin, R. (2021). U-NET: convolution neural network for lung image segmentation and classification in chest X-ray images. INFOCOMP: Journal of Computer Science, 20(1), 101-108.
  15. Asaithambi, A., & Thamilarasi, V. (2023, March). Classification of Lung Chest X-Ray Images Using Deep Learning with Efficient Optimizers. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0465-0469). IEEE.
  16. Jadhav, S., Machale, A., Mharnur, P., Munot, P., & Math, S. (2019, September). Text based stress detection techniques analysis using social media. In 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA) (pp. 1-5). IEEE.
  17. Anitha, C., Tellur, A., Rao, K. B., Kumbhar, V., Gopi, T., Jadhav, S., & Vidhya, R. G. (2024). Enhancing Cyber-Physical Systems Dependability through Integrated CPS-IoT Monitoring. International Research Journal of Multidisciplinary Scope, 5(2), 706-713.
  18. Kiran, A., Sonker, A., Jadhav, S., Jadhav, M. M., Naga Ramesh, J. V., & Muniyandy, E. (2024). Secure Communications with THz Reconfigurable Intelligent Surfaces and Deep Learning in 6G Systems. Wireless Personal Communications, 1-17.
  19. Thepade, D. S., Mandal, P. R., & Jadhav, S. (2015). Performance Comparison of Novel Iris Recognition Techniques Using Partial Energies of Transformed Iris Images and Enegy CompactionWith Hybrid Wavelet Transforms. In Annual IEEE India Conference (INDICON).
  20. Vandana, C. P., Basha, S. A., Madiajagan, M., Jadhav, S., Matheen, M. A., & Maguluri, L. P. (2024). IoT resource discovery based on multi faected attribute enriched CoAP: smart office seating discovery. Wireless Personal Communications, 1-18.
  21. Jadhav, S., Durairaj, M., Reenadevi, R., Subbulakshmi, R., Gupta, V., & Ramesh, J. V. N. (2024). Spatiotemporal data fusion and deep learning for remote sensing-based sustainable urban planning. International Journal of System Assurance Engineering and Management, 1-9.
  22. Jadhav, S., Chaudhari, V., Barhate, P., Deshmukh, K., & Agrawal, T. (2021). Extreme Gradient Boosting for Predicting Stock Price Direction in Context of Indian Equity Markets. In Intelligent Sustainable Systems: Selected Papers of WorldS4 2021, Volume 2 (pp. 321-330). Singapore: Springer Nature Singapore.
  23. Jadhav, S., Chaudhari, V., Barhate, P., Deshmukh, K., & Agrawal, T. (2021). REVIEW PAPER ON: ALGORITHMIC TRADING USING ARTIFICIAL INTELLEGENCE.
  24. Jadhav, S., Chaudhari, V., Barhate, P., Deshmukh, K., & Agrawal, T. (2021). in Context of Indian Equity Markets. Intelligent Sustainable Systems: Selected Papers of WorldS4 2021, Volume 2, 334, 321.
  25. Jadhav, S. R., Bishnoi, A., Safarova, N., Khan, F., Aurangzeb, K., & Alhussein, M. (2024). Dual-Attention Based Multi-Path Approach for Intensifying Stock Market Forecasting. Fluctuation and Noise Letters, 23(02), 2440009.
  26. Vishwanath, B., & Vaddepalli, S. (2023). The future of work: Implications of artificial intelligence on hr practices. Tuijin Jishu/Journal of Propulsion Technology, 44(3), 1711-1724.
  27. Surendar Vaddepalli, D. B. V. (2025). ENTREPRENEURIAL ECOSYSTEMS IN THE GCC-ASSESSING SUPPORT SYSTEMS FOR WOMEN AND DISABLED ENTREPRENEURS IN OMAN. Machine Intelligence Research, 19(1), 126-143.
  28. Vaddepalli, S., & Vishwanath, B. (2024). MERGERS AND ACQUISITIONS: DRIVERS, CHALLENGES, AND PERFORMANCE OUTCOMES IN GCC NATIONS. International Journal of Central Banking, 20(1), 298-310.
  29. Sangam, V. G., Priyadarshini, S. H., Anand, N., Prathibha, P., Purohit, P., & Nalamitha, R. (2021, June). Early Detection of Diabetic Foot Ulcer. In Journal of Physics: Conference Series (Vol. 1937, No. 1, p. 012049). IOP Publishing.
  30. Kumar, C. R., Vijayalakshmi, B., Priyadarshini, S. H., Sikdar, S., Bhat, S. N., & Neelam, M. (2020). Standing wheelchair with voice recognition system. J. Crit. Rev, 7, 2042-2047.
  31. Priyadarshini, S. H., Dutt, D. N., & Rajan, A. P. (2019). Nonlinear Processing of Wrist Pulse Signals to Distinguish Diabetic and Non-Diabetic Subjects. Int. J. Eng. Adv. Technol., 9(1), 7105-7110.
  32. Priyadarshini, S. H., Poojitha, S., Vinay, K. V., & VA, A. D. (2023, October). AQUASENSE: Sensor Based Water Quality Monitoring Device. In 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 1786-1789). IEEE.
  33. Padma, C. R., Priyadarshini, S. H., Nanditha, H. G., Pavithra, G., & Manjunath, T. C. (2022, August). Design & Development of micro-controlled system using VHDL with the help of UART Tx & Rx. In 2022 2nd Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-11). IEEE.
  34. Rao, M. R., Mangu, B., & Kanth, K. S. (2007, December). Space vector pulse width modulation control of induction motor. In IET-UK International Conference on Information and Communication Technology in Electrical Sciences (ICTES 2007) (pp. 349-354). Stevenage UK: IET.
  35. Rao, M. R., & Prasad, P. V. N. (2014). Modelling and Implementation of Sliding Mode Controller for PMBDC Motor Drive. International journal of advanced research in electrical, electronics and instrumentation engineering, 3(6).
  36. Sameera, K., & MVR, S. A. R. (2014). Improved power factor and reduction of harmonics by using dual boost converter for PMBLDC motor drive. Int J Electr Electron Eng Res, 4(5), 43-51.
  37. Srinivasu, B., Prasad, P. V. N., & Rao, M. R. (2006, December). Adaptive controller design for permanent magnet linear synchronous motor control system. In 2006 International Conference on Power Electronic, Drives and Energy Systems (pp. 1-6). IEEE.
  38. Al-Ghanimi, M. G., Hanif, O., Jain, M. V., Kumar, A. S., Rao, R., Kavin, R., … & Hossain, M. A. (2022, December). Two TS-Fuzzy Controllers based Direct Torque Control of 5-Phase Induction Motor. In 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) (pp. 1-6). IEEE.
  39. Prathap, P. B., & Saara, K. (2024). Quantifying efficacy of the fiber bragg grating sensors in medical applications: a survey. Journal of Optics, 53(5), 4180-4201.
  40. Kumar, T. V. (2024). A Comprehensive Empirical Study Determining Practitioners’ Views on Docker Development Difficulties: Stack Overflow Analysis.
  41. Kumar, T. V. (2024). A New Framework and Performance Assessment Method for Distributed Deep Neural NetworkBased Middleware for Cyberattack Detection in the Smart IoT Ecosystem.
  42. Arora, P., & Bhardwaj, S. (2017). A Very Safe and Effective Way to Protect Privacy in Cloud Data Storage Configurations.
  43. Arora, P., & Bhardwaj, S. (2017). Combining Internet of Things and Wireless Sensor Networks: A Security-based and Hierarchical Approach.
  44. Arora, P., & Bhardwaj, S. (2017). Enhancing Security using Knowledge Discovery and Data Mining Methods in Cloud Computing.
  45. Yendluri, D. K., Ponnala, J., Tatikonda, R., Kempanna, M., Thatikonda, R., & Bhuvanesh, A. (2023, November). Role of rpa & ai in optimizing network field services. In 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) (pp. 1-6). IEEE.
  46. Yendluri, D. K., Ponnala, J., Thatikonda, R., Kempanna, M., Tatikonda, R., & Bhuvanesh, A. (2023, November). Impact of Robotic Process Automation on Enterprise Resource Planning Systems. In 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM) (pp. 1-6). IEEE.
  47. Sidharth, S. (2021). MULTI-CLOUD ENVIRONMENTS: MITIGATING SECURITY RISKS IN DISTRIBUTED ARCHITECTURES.
  48. Sidharth, S. (2020). THE GROWING THREAT OF DEEPFAKES: IMPLICATIONS FOR SECURITY AND PRIVACY.
  49. Sidharth, S. (2019). QUANTUM-ENHANCED ENCRYPTION TECHNIQUES FOR CLOUD DATA PROTECTION.
  50. Sidharth, S. (2019). SECURING CLOUD-NATIVE MICROSERVICES WITH SERVICE MESH TECHNOLOGIES.
  51. Sidharth, S. (2019). DATA LOSS PREVENTION (DLP) STRATEGIES IN CLOUD-HOSTED APPLICATIONS.
  52. Sidharth, S. (2018). RANSOMWARE TRENDS AND EFFECTIVE MITIGATION TECHNIQUES IN 2018.
  53. Sidharth, S. (2018). POST-QUANTUM CRYPTOGRAPHY: PREPARING FOR A QUANTUM COMPUTING ERA.
  54. Sidharth, S. (2017). CYBERSECURITY STRATEGIES FOR IOT DEVICES IN SMART CITIES.
  55. Sidharth, S. (2017). ACCESS CONTROL MODELS FOR SECURE HYBRID CLOUD DEPLOYMENT.
  56. Sidharth, S. (2017). MACHINE LEARNING ALGORITHMS FOR REALTIME MALWARE DETECTION.
  57. Ara, T., Ambareen, J., Venkatesan, S., Geetha, M., & Bhuvanesh, A. (2024). An energy efficient selection of cluster head and disease prediction in IoT based smart agriculture using a hybrid artificial neural network model. Measurement: Sensors, 32, 101074.
  58. Divyashree, H. S., Avinash, N., Manjunatha, B. N., Vishesh, J., & Mamatha, M. (2024). Enhancing secrecy using hybrid elliptic curve cryptography and Diffie Hellman key exchange approach and Young’s double slit experiment optimizer based optimized cross layer in multihop wireless network. Measurement: Sensors, 31, 100967.
  59. NR, D., GK, D. S., & Kumar Pareek, D. P. (2022, February). A Framework for Food recognition and predicting its Nutritional value through Convolution neural network. In Proceedings of the International Conference on Innovative Computing & Communication (ICICC).
  60. Prasath, D. S., & Selvakumar, A. (2015). A Novel Iris Image Retrieval with Boundary Based Feature Using Manhattan Distance Classifier. International Journal Of Innovative Technology And Creative Engineering (Issn: 2045-8711) Vol, 5.
  61. Nirmala, K., & Prasath, S. (2020). Probabilistic mceliece public-key cryptography based identity authentication for secured communication in VANET. Solid State Technology, 63(6), 10167-10182.
  62. Sivasankaran, P., & Dhanaraj, K. R. (2024). Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm. Revue d’Intelligence Artificielle, 38(1).
  63. Pannirselvam, S., & Prasath, S. (2015). A Novel Technique for Face Recognition and Retrieval using Fiducial Point Features. Procedia Computer Science, 47, 301-310.

Tamilselvi, R., Mohanasathiya, K. S., & Prasath, S. (2024). Developed a Smooth Support Vector Machine to Predict the Crop Production in Alluvial Soil and Red Soil Regions of Tamil Nadu India [J]. Naturalista Campano, 28(1), 279-297.

Abstract

Snake encounters in both human-populated and wildlife areas present major risks to public safety and biodiversity. Each year, thousands of people suffer from snakebites, often resulting in severe injuries or even fatalities. Simultaneously, many snakes are harmed or killed due to fear and lack of awareness. Traditional snake detection methods, such as manual inspection or visual observation, are slow, error-prone, and inefficient. These outdated techniques delay response times and increase the likelihood of dangerous human-snake interactions.Snake detection system

To address this challenge, we propose a Real-Time Snake Detection and Alert System that leverages advanced Artificial Intelligence (AI) and Machine Learning (ML). Our solution uses the YOLO (You Only Look Once) object detection algorithm to accurately identify the presence of snakes in pre-recorded videos or still images. The system instantly analyzes visual inputs and triggers an automated alarm if a snake is detected. This real-time alert mechanism ensures timely warnings, enhancing public safety in snake-prone zones without requiring continuous human monitoring.

Introduction

Our AI-driven approach significantly reduces the risk of snakebite incidents while promoting the protection of wildlife. YOLO’s high-speed processing and accuracy make it ideal for real-time deployment in rural areas, agricultural fields, residential zones, and nature reserves.

Incorporating deep learning into wildlife monitoring not only improves safety but also contributes to ecological balance. By automating snake detection, our system empowers communities, reduces false alarms, and ensures a faster response to threats. This technology is a scalable, efficient, and cost-effective tool for modern wildlife management and public safety.

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