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Speeding Classification by a Deep Learning Audio Analysis System Optimized by the Reptile Search Algorithm

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Proceedings of International Joint Conference on Advances in Computational Intelligence (IJCACI 2022)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Speeding is a dangerous traffic transgression as it causes a large portion of casualties in traffic. Traditional approaches based on radar and lidar are effective to some extent, but the room for improvement is noticeable. There is also a factor of cost with this type of equipment. With advancements in artificial intelligence (AI) techniques, the capabilities of autonomous, precise, and robust systems are becoming achievable. The role of AI is to analyze the input from video or audio recorders. For this work, the focus was on audio recordings. This research proposes a deep neural network (DNN) approach for vehicle speed optimization along with a metaheuristic approach for the optimization of the DNN hyperparameters. A swarm-based algorithm was chosen for hyperparameter optimization that has been regarded as an efficient approach for solving non-deterministic polynomial time (NP) hard problems. Chosen algorithm is the reptile search algorithm. The problems of hyperparameter optimization and the real-world problem of speeding classification belong to this group of problems. The model was compared to other DNN-metaheuristic high-performing solutions from which it was deduced that the proposed approach is highly promising.

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Notes

  1. 1.

    http://slobodan.ucg.ac.me/science/vs13/.

References

  1. Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile search algorithm (rsa): a nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158

    Article  Google Scholar 

  2. Adam SP, Alexandropoulos SAN, Pardalos PM, Vrahatis MN (2019) No free lunch theorem: A review. Approximation Optim Algorithms Complex Appl 57–82

    Google Scholar 

  3. Bacanin N, Budimirovic N, Venkatachalam K, Jassim HS, Zivkovic M, Askar S, Abouhawwash M (2023) Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection. Heliyon 9(4)

    Google Scholar 

  4. Bacanin N, Jovanovic L, Zivkovic M, Kandasamy V, Antonijevic M, Deveci M, Strumberger I (2023) Multivariate energy forecasting via metaheuristic tuned long-short term memory and gated recurrent unit neural networks. Inf Sci 119122

    Google Scholar 

  5. Bacanin N, Stoean C, Zivkovic M, Rakic M, Strulak-Wójcikiewicz R, Stoean R (2023) On the benefits of using metaheuristics in the hyperparameter tuning of deep learning models for energy load forecasting. Energies 16(3):1434

    Article  Google Scholar 

  6. Bacanin N, Stoean R, Zivkovic M, Petrovic A, Rashid TA, Bezdan T (2021) Performance of a novel chaotic firefly algorithm with enhanced exploration for tackling global optimization problems: application for dropout regularization. Mathematics 9(21):2705

    Article  Google Scholar 

  7. Bacanin N, Venkatachalam K, Bezdan T, Zivkovic M, Abouhawwash M (2023) A novel firefly algorithm approach for efficient feature selection with covid-19 dataset. Microprocess Microsyst 98:104778

    Article  Google Scholar 

  8. Bacanin N, Zivkovic M, Al-Turjman F, Venkatachalam K, Trojovskỳ P, Strumberger I, Bezdan T (2022) Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application. Sci Rep 12(1):6302

    Article  Google Scholar 

  9. Bacanin N, Zivkovic M, Antonijevic M, Venkatachalam K, Lee J, Nam Y, Marjanovic M, Strumberger I, Abouhawwash M (2023) Addressing feature selection and extreme learning machine tuning by diversity-oriented social network search: an application for phishing websites detection. Complex Syst 1–36

    Google Scholar 

  10. Castillo-Manzano JI, Castro-Nuño M, Lopez-Valpuesta L, Vassallo FV (2019) The complex relationship between increases to speed limits and traffic fatalities: evidence from a meta-analysis. Saf Sci 111:287–297

    Article  Google Scholar 

  11. Cheng R, Li M, Tian Y, Xiang X, Zhang X, Yang S, Jin Y, Yao X (2018) Benchmark functions for the cec’2018 competition on many-objective optimization. Tech Rep

    Google Scholar 

  12. Delaney A, Ward H, Cameron M, Williams AF (2005) Controversies and speed cameras: lessons learnt internationally. J Pub Health Policy 26:404–415

    Article  Google Scholar 

  13. Djukanović S, Bulatović N, Čavor I (2022) A dataset for audio-video based vehicle speed estimation. In: 2022 30th telecommunications forum (TELFOR). IEEE, pp 1–4

    Google Scholar 

  14. Duman E, Uysal M, Alkaya AF (2012) Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf Sci 217:65–77

    Article  MathSciNet  Google Scholar 

  15. Ellis A (2003) A deterrence theory of punishment. Philos Q 53(212):337–351

    Article  Google Scholar 

  16. Hauer E, Ahlin F, Bowser J (1982) Speed enforcement and speed choice. Accid Anal Prev 14(4):267–278

    Article  Google Scholar 

  17. Jovanovic G, Perisic M, Bacanin N, Zivkovic M, Stanisic S, Strumberger I, Alimpic F, Stojic A (2023) Potential of coupling metaheuristics-optimized-xgboost and shap in revealing pahs environmental fate. Toxics 11(4):394

    Article  Google Scholar 

  18. Jovanovic L, Bacanin N, Zivkovic M, Antonijevic M, Jovanovic B, Sretenovic MB, Strumberger I (2023) Machine learning tuning by diversity oriented firefly metaheuristics for industry 4.0. Expert Systems p e13293

    Google Scholar 

  19. Jovanovic L, Jovanovic D, Bacanin N, Jovancai Stakic A, Antonijevic M, Magd H, Thirumalaisamy R, Zivkovic M (2022) Multi-step crude oil price prediction based on lstm approach tuned by salp swarm algorithm with disputation operator. Sustainability 14(21):14616

    Article  Google Scholar 

  20. Jovanovic L, Jovanovic D, Antonijevic M, Nikolic B, Bacanin N, Zivkovic M, Strumberger I (2023) Improving phishing website detection using a hybrid two-level framework for feature selection and xgboost tuning. J Web Eng 543–574

    Google Scholar 

  21. Jovanovic L, Jovanovic G, Perisic M, Alimpic F, Stanisic S, Bacanin N, Zivkovic M, Stojic A (2023) The explainable potential of coupling metaheuristics-optimized-xgboost and shap in revealing vocs’ environmental fate. Atmosphere 14(1):109

    Article  Google Scholar 

  22. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks. vol. 4. IEEE, pp 1942–1948

    Google Scholar 

  23. Le N, Rathour VS, Yamazaki K, Luu K, Savvides M (2022) Deep reinforcement learning in computer vision: a comprehensive survey. Artif Intell Rev 1–87

    Google Scholar 

  24. Mejia H, Palomo E, López-Rubio E, Pineda I, Fonseca R (2021) Vehicle speed estimation using computer vision and evolutionary camera calibration. In: NeurIPS 2021 workshop LatinX in AI

    Google Scholar 

  25. Mirjalili S (2016) Sca: a sine cosine algorithm for solving optimization problems. Knowl-based Syst 96:120–133

    Article  Google Scholar 

  26. Mirjalili S, Mirjalili S (2019) Genetic algorithm. Evolutionary Algorithms Neural Netw Theory Appl 43–55

    Google Scholar 

  27. Montavon G, Samek W, Müller KR (2018) Methods for interpreting and understanding deep neural networks. Digit Signal Process 73:1–15

    Article  MathSciNet  Google Scholar 

  28. Petrovic A, Damaševičius R, Jovanovic L, Toskovic A, Simic V, Bacanin N, Zivkovic M, Spalević P (2023) Marine vessel classification and multivariate trajectories forecasting using metaheuristics-optimized extreme gradient boosting and recurrent neural networks. Appl Sci 13(16):9181

    Article  Google Scholar 

  29. Ralph K, Barajas JM, Johnson-Rodriguez A, Delbosc A, Muir C (2022) The end of speed traps and ticket quotas: Re-framing and reforming traffic cameras to increase support. J Plann Educ Res 0739456X221138073

    Google Scholar 

  30. Savanović N, Toskovic A, Petrovic A, Zivkovic M, Damaševičius R, Jovanovic L. Bacanin N, Nikolic B (2023) Intrusion detection in healthcare 4.0 internet of things systems via metaheuristics optimized machine learning. Sustainability 15(16):12563

    Google Scholar 

  31. Stankovic M, Jovanovic L, Bacanin N, Zivkovic M, Antonijevic M, Bisevac P (2022) Tuned long short-term memory model for ethereum price forecasting through an arithmetic optimization algorithm. In: International conference on innovations in bio-inspired computing and applications. Springer, Berlin, pp 327–337

    Google Scholar 

  32. Stoean C, Zivkovic M, Bozovic A, Bacanin N, Strulak-Wójcikiewicz R, Antonijevic M, Stoean R (2023) Metaheuristic-based hyperparameter tuning for recurrent deep learning: Application to the prediction of solar energy generation. Axioms 12(3):266

    Article  Google Scholar 

  33. Walczak S (2019) Artificial neural networks. In: Advanced methodologies and technologies in artificial intelligence, computer simulation, and human-computer interaction. IGI global, pp 40–53

    Google Scholar 

  34. Wang J, Cicchino JB (2023) Changes in speeding on virginia roads during the beginning of the covid-19 pandemic. Traffic Inj Prevention 24(1):38–43

    Article  Google Scholar 

  35. Wei Y, Hu D, Tian Y, Li X (2022) Learning in audio-visual context: a review, analysis, and new perspective. arXiv preprint arXiv:2208.09579

  36. Yang XS, Hossein Gandomi A (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  37. Yang XS, Slowik A (2020) Firefly algorithm. In: Swarm intelligence algorithms. CRC Press, pp 163–174

    Google Scholar 

  38. Zivkovic M, Bacanin N, Antonijevic M, Nikolic B, Kvascev G, Marjanovic M, Savanovic N (2022) Hybrid cnn and xgboost model tuned by modified arithmetic optimization algorithm for covid-19 early diagnostics from x-ray images. Electronics 11(22):3798

    Article  Google Scholar 

  39. Zivkovic M, Bacanin N, Venkatachalam K, Nayyar A, Djordjevic A, Strumberger I, Al-Turjman F (2021) Covid-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain Cities Soc 66:102669

    Article  Google Scholar 

  40. Zivkovic M, Stoean C, Chhabra A, Budimirovic N, Petrovic A, Bacanin N (2022) Novel improved salp swarm algorithm: an application for feature selection. Sensors 22(5):1711

    Article  Google Scholar 

  41. Zivkovic M, Tair M, Venkatachalam K, Bacanin N, Hubálovskỳ Š, Trojovskỳ P (2022) Novel hybrid firefly algorithm: an application to enhance xgboost tuning for intrusion detection classification. PeerJ Comput Sci 8:e956

    Article  Google Scholar 

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Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11010, Belgrade, Serbia

    Tea Dogandzic, Aleksandar Petrovic, Luka Jovanovic, Nebojsa Bacanin, Andjela Jovanovic & Miodrag Zivkovic

Authors
  1. Tea Dogandzic
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  2. Aleksandar Petrovic
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  3. Luka Jovanovic
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  4. Nebojsa Bacanin
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  5. Andjela Jovanovic
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  6. Miodrag Zivkovic
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Corresponding author

Correspondence to Tea Dogandzic .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shorif Uddin

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

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Dogandzic, T., Petrovic, A., Jovanovic, L., Bacanin, N., Jovanovic, A., Zivkovic, M. (2024). Speeding Classification by a Deep Learning Audio Analysis System Optimized by the Reptile Search Algorithm. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. IJCACI 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-97-0180-3_7

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