Research Article
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Year 2023, Volume: 4 Issue: 1, 38 - 47, 25.06.2023

Mahmut Dirik

https://doi.org/10.55195/jscai.1310837

Abstract

References

  • C. A. Matticks, J. J. Westwater, H. N. Himel, R. F. Morgan, and R. F. Edlich, ‘Health Risks to Fire Fighters’, J Burn Care Rehabil, vol. 13, no. 2, pp. 223–235, Mar. 1992, doi: 10.1097/00004630-199203000-00010.
  • A. B. Morgan and J. W. Gilman, ‘An overview of flame retardancy of polymeric materials: Application, technology, and future directions’, Fire Mater, vol. 37, no. 4, pp. 259–279, Jun. 2013, doi: 10.1002/FAM.2128.
  • M. Shokouhi, K. Nasiriani, H. Khankeh, H. Fallahzadeh, and D. Khorasani-Zavareh, ‘Exploring barriers and challenges in protecting residential fire-related injuries: a qualitative study’, J Inj Violence Res, vol. 11, no. 1, p. 81, 2019, doi: 10.5249/JIVR.V11I1.1059.
  • R. Olawoyin, ‘Nanotechnology: The future of fire safety’, 2018, doi: 10.1016/j.ssci.2018.08.016.
  • Y. Awad, M. Kohail, M. A. Khalaf, and Y. A. Ali, ‘Effect of fire extinguishing techniques on the strength of RC columns’, Asian Journal of Civil Engineering, vol. 23, no. 1, pp. 113–123, Jan. 2022, doi: 10.1007/S42107-021-00414-8/FIGURES/12.
  • F. Dubocq et al., ‘Organic contaminants formed during fire extinguishing using different firefighting methods assessed by nontarget analysis’, Environmental Pollution, vol. 265, p. 114834, Oct. 2020, doi: 10.1016/J.ENVPOL.2020.114834.
  • Y. Zhou, R. Bu, J. Gong, X. Zhang, C. Fan, and X. Wang, ‘Assessment of a clean and efficient fire-extinguishing technique: Continuous and cycling discharge water mist system’, J Clean Prod, vol. 182, pp. 682–693, May 2018, doi: 10.1016/J.JCLEPRO.2018.02.046.
  • M. Rajczyk et al., ‘Application of acoustic oscillations in flame extinction in a presence of obstacle’, J Phys Conf Ser, vol. 1101, no. 1, p. 012023, Oct. 2018, doi: 10.1088/1742-6596/1101/1/012023.
  • A. B. Morgan and J. W. Gilman, ‘An overview of flame retardancy of polymeric materials: application, technology, and future directions’, Fire Mater, vol. 37, no. 4, pp. 259–279, Jun. 2013, doi: 10.1002/FAM.2128.
  • V. Sharifi, A. M. Kempf, and C. Beck, ‘Large-Eddy Simulation of Acoustic Flame Response to High-Frequency Transverse Excitations’, https://doi.org/10.2514/1.J056818, vol. 57, no. 1, pp. 327–340, Nov. 2018, doi: 10.2514/1.J056818.
  • Y. S. Taspinar, M. Koklu, and M. Altin, ‘Acoustic-Driven Airflow Flame Extinguishing System Design and Analysis of Capabilities of Low Frequency in Different Fuels’, Fire Technol, vol. 58, no. 3, pp. 1579–1597, May 2022, doi: 10.1007/S10694-021-01208-9/TABLES/4.
  • A. N. Friedman and S. I. Stoliarov, ‘Acoustic extinction of laminar line-flames’, Fire Saf J, vol. 93, pp. 102–113, Oct. 2017, doi: 10.1016/J.FIRESAF.2017.09.002.
  • X. Shi, Y. Zhang, X. Chen, Y. Zhang, Q. Ma, and G. Lin, ‘The response of an ethanol pool fire to transverse acoustic waves’, Fire Saf J, vol. 125, p. 103416, Oct. 2021, doi: 10.1016/J.FIRESAF.2021.103416.
  • C. Xiong, Y. Liu, C. Xu, and X. Huang, ‘Acoustical Extinction of Flame on Moving Firebrand for the Fire Protection in Wildland–Urban Interface’, Fire Technol, vol. 57, no. 3, pp. 1365–1380, May 2021, doi: 10.1007/S10694-020-01059-W/FIGURES/11.
  • J. O’Connor, V. Acharya, and T. Lieuwen, ‘Transverse combustion instabilities: acoustic, fluid mechanic, and flame processes’, Prog Energy Combust Sci, vol. 49, pp. 1–39, Aug. 2015, doi: 10.1016/j.pecs.2015.01.001.
  • A. N. Friedman and S. I. Stoliarov, ‘Acoustic extinction of laminar line-flames’, Fire Saf J, vol. 93, pp. 102–113, Oct. 2017, doi: 10.1016/j.firesaf.2017.09.002.
  • F. Baillot and F. Lespinasse, ‘Response of a laminar premixed V-flame to a high-frequency transverse acoustic field’, Combust Flame, vol. 161, no. 5, pp. 1247–1267, May 2014, doi: 10.1016/J.COMBUSTFLAME.2013.11.009.
  • E. Beisner et al., ‘Acoustic Flame Suppression Mechanics in a Microgravity Environment’, Microgravity Sci Technol, vol. 27, no. 3, pp. 141–144, Jun. 2015, doi: 10.1007/S12217-015-9422-4/FIGURES/5.
  • T. Y. T. K. M Tunabe, ‘Numerical Simulation on the Flame Propagation in Acoustic Fields’, JASMA, vol. 23, pp. 371–375, 2008.
  • M. Z. Abbasi, P. S. Wilson, and O. A. Ezekoye, ‘Modeling acoustic propagation in a compartment fire’, J Acoust Soc Am, vol. 134, no. 5, pp. 4218–4218, Nov. 2013, doi: 10.1121/1.4831486.
  • M. Z. Abbasi, O. A. Ezekoye, and P. S. Wilson, ‘Measuring the acoustic response of a compartment fire’, Proceedings of Meetings on Acoustics, vol. 19, 2013, doi: 10.1121/1.4799626.
  • M. Z. Abbasi, P. S. Wilson, and O. A. Ezekoye, ‘Change in acoustic impulse response of a room due to a fire’, J Acoust Soc Am, vol. 147, no. 6, p. EL546, Jun. 2020, doi: 10.1121/10.0001415.
  • M. J. Sousa, A. Moutinho, and M. Almeida, ‘Classification of potential fire outbreaks’, Expert Syst Appl, vol. 129, pp. 216–232, Sep. 2019, doi: 10.1016/J.ESWA.2019.03.030.
  • Y. Ye, X. Luo, C. Dong, Y. Xu, and Z. Zhang, ‘Numerical and experimental investigation of soot suppression by acoustic oscillated combustion’, ACS Omega, vol. 5, no. 37, pp. 23866–23875, Sep. 2020, doi: 10.1021/ACSOMEGA.0C03107/SUPPL_FILE/AO0C03107_SI_006.AVI.
  • J. Lloret, M. Garcia, D. Bri, and S. Sendra, ‘A wireless sensor network deployment for rural and forest fire detection and verification’, Sensors, vol. 9, no. 11, pp. 8722–8747, Nov. 2009, doi: 10.3390/S91108722.
  • B. L. Wenning, D. Pesch, A. Timm-Giel, and C. Görg, ‘Environmental monitoring aware routing: Making environmental sensor networks more robust’, Telecommun Syst, vol. 43, no. 1–2, pp. 3–11, Feb. 2010, doi: 10.1007/S11235-009-9191-8.
  • A. A. A. Alkhatib, ‘A Review on Forest Fire Detection Techniques’:, http://dx.doi.org/10.1155/2014/597368, vol. 2014, Mar. 2014, doi: 10.1155/2014/597368.
  • P. Barmpoutis, P. Papaioannou, K. Dimitropoulos, and N. Grammalidis, ‘A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing’, Sensors 2020, Vol. 20, Page 6442, vol. 20, no. 22, p. 6442, Nov. 2020, doi: 10.3390/S20226442.
  • K. Grover, D. Kahali, S. Verma, and B. Subramanian, ‘WSN-Based System for Forest Fire Detection and Mitigation’, pp. 249–260, 2020, doi: 10.1007/978-981-13-7968-0_19.
  • S. J. Chen, D. C. Hovde, K. A. Peterson, and A. W. Marshall, ‘Fire detection using smoke and gas sensors’, Fire Saf J, vol. 42, no. 8, pp. 507–515, Nov. 2007, doi: 10.1016/J.FIRESAF.2007.01.006.
  • G. H. Mitri, I. Z. Gitas, G. H. Mitri, and I. Z. Gitas, ‘Fire type mapping using object-based classification of Ikonos imagery’, Int J Wildland Fire, vol. 15, no. 4, pp. 457–462, Dec. 2006, doi: 10.1071/WF05085.
  • I. Z. Gitas, G. H. Mitri, and G. Ventura, ‘Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery’, Remote Sens Environ, vol. 92, no. 3, pp. 409–413, Aug. 2004, doi: 10.1016/J.RSE.2004.06.006.
  • M. G. Cruz, J. S. Gould, J. J. Hollis, and W. L. McCaw, ‘A Hierarchical Classification of Wildland Fire Fuels for Australian Vegetation Types’, Fire 2018, Vol. 1, Page 13, vol. 1, no. 1, p. 13, Apr. 2018, doi: 10.3390/FIRE1010013.
  • ‘Acoustic Extinguisher Fire Dataset | Kaggle’. https://www.kaggle.com/datasets/muratkokludataset/acoustic-extinguisher-fire-dataset (accessed May 27, 2022).
  • Y. S. Taspinar, M. Koklu, and M. Altin, ‘Classification of flame extinction based on acoustic oscillations using artificial intelligence methods’, Case Studies in Thermal Engineering, vol. 28, Dec. 2021, doi: 10.1016/J.CSITE.2021.101561.
  • Y. S. Taspinar, M. Koklu, and M. Altin, ‘Acoustic-Driven Airflow Flame Extinguishing System Design and Analysis of Capabilities of Low Frequency in Different Fuels’, Fire Technol, May 2022, doi: 10.1007/S10694-021-01208-9.
  • M. Koklu and Y. S. Taspinar, ‘Determining the Extinguishing Status of Fuel Flames with Sound Wave by Machine Learning Methods’, IEEE Access, vol. 9, pp. 86207–86216, 2021, doi: 10.1109/ACCESS.2021.3088612.
  • W. S. McCulloch and W. Pitts, ‘A logical calculus of the ideas immanent in nervous activity’, Bull Math Biophys, vol. 5, no. 4, pp. 115–133, Dec. 1943, doi: 10.1007/BF02478259/METRICS.
  • ‘Hebb, D. O. The organization of behavior: A neuropsychological theory. New York: John Wiley and Sons, Inc., 1949. 335 p. $4.00’, Sci Educ, vol. 34, no. 5, pp. 336–337, Dec. 1950, doi: 10.1002/SCE.37303405110.
  • F. Rosenblatt, ‘The perceptron: A probabilistic model for information storage and organization in the brain’, Psychol Rev, vol. 65, no. 6, pp. 386–408, Nov. 1958, doi: 10.1037/H0042519.
  • C. A. Tudor, ‘Analysis of the Rosenblatt process’, ESAIM: Probability and Statistics, vol. 12, pp. 230–257, Oct. 2008, doi: 10.1051/PS:2007037.
  • A. Shmilovici, ‘Support Vector Machines’, Data Mining and Knowledge Discovery Handbook, pp. 231–247, 2009, doi: 10.1007/978-0-387-09823-4_12.
  • A. v. Joshi, ‘Support Vector Machines’, Machine Learning and Artificial Intelligence, pp. 89–99, 2023, doi: 10.1007/978-3-031-12282-8_8.
  • Ingo. Steinwart and Andreas. Christmann, ‘Support vector machines’, p. 601, 2008.
  • G. Teles, J. J. P. C. Rodrigues, R. A. L. Rabêlo, and S. A. Kozlov, ‘Comparative study of support vector machines and random forests machine learning algorithms on credit operation’, Softw Pract Exp, vol. 51, no. 12, pp. 2492–2500, Dec. 2021, doi: 10.1002/SPE.2842.
  • S. Kim, Z. Yu, R. M. Kil, and M. Lee, ‘Deep learning of support vector machines with class probability output networks’, Neural Networks, vol. 64, pp. 19–28, Apr. 2015, doi: 10.1016/J.NEUNET.2014.09.007.
  • B. Schölkopf, ‘SVMs - A practical consequence of learning theory’, IEEE Intelligent Systems and Their Applications, vol. 13, no. 4, pp. 18–21, Jul. 1998, doi: 10.1109/5254.708428.
  • ‘Support Vector Machines for Regression.’, Support Vector Machines, pp. 330–351, Aug. 2008, doi: 10.1007/978-0-387-77242-4_9.
  • S. Nusinovici et al., ‘Logistic regression was as good as machine learning for predicting major chronic diseases’, J Clin Epidemiol, vol. 122, pp. 56–69, Jun. 2020, doi: 10.1016/J.JCLINEPI.2020.03.002.
  • T. Rymarczyk, E. Kozłowski, G. Kłosowski, and K. Niderla, ‘Logistic Regression for Machine Learning in Process Tomography’, Sensors 2019, Vol. 19, Page 3400, vol. 19, no. 15, p. 3400, Aug. 2019, doi: 10.3390/S19153400.
  • E. Bisong, ‘Logistic Regression’, Building Machine Learning and Deep Learning Models on Google Cloud Platform, pp. 243–250, 2019, doi: 10.1007/978-1-4842-4470-8_20.
  • S. Orozco-Arias, J. S. Piña, R. Tabares-Soto, L. F. Castillo-Ossa, R. Guyot, and G. Isaza, ‘Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements’, doi: 10.3390/pr8060638.
  • S. Adhikari, S.-L. Normand, J. Bloom, D. Shahian, and S. Rose, ‘Revisiting performance metrics for prediction with rare outcomes’, doi: 10.1177/09622802211038754.
  • M. Steurer, R. J. Hill, and N. Pfeifer, ‘Metrics for evaluating the performance of machine learning based automated valuation models’, Journal of Property Research, vol. 38, no. 2, pp. 99–129, 2021, doi: 10.1080/09599916.2020.1858937.
  • A. Rácz, D. Bajusz, and K. Héberger, ‘molecules Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics’, doi: 10.3390/molecules24152811.
  • M. Dirik, ‘Optimized Anfis Model with Hybrid Metaheuristic Algorithms for Facial Emotion Recognition’, International Journal of Fuzzy Systems, pp. 1–12, Oct. 2022, doi: 10.1007/S40815-022-01402-Z/FIGURES/5.
  • D. Chicco and G. Jurman, ‘The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation’, BMC Genomics, vol. 21, no. 1, pp. 6-1-6–13, Jan. 2020, doi: 10.1186/s12864-019-6413-7.
  • D. M. W. Powers, ‘Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation’, Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011, Accessed: Oct. 28, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:55767944#id-name=S2CID
  • T. Fawcett, ‘An Introduction to ROC Analysis’, Pattern Recognit Lett, vol. 27, no. 8, pp. 861–874, Jun. 2006, doi: 10.1016/j.patrec.2005.10.010.
  • D. Chicco and G. Jurman, ‘The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation’, BMC Genomics, vol. 21, no. 1, pp. 6-1-6–13, Jan. 2020, doi: 10.1186/s12864-019-6413-7.
  • S. Josephine Isabella, S. Srinivasan, and G. Suseendran, ‘An Efficient Study of Fraud Detection System Using Ml Techniques’, in Lecture Notes in Networks and Systems, Springer, 2020, pp. 59–67. doi: 10.1007/978-981-15-3284-9_8. [ A. A. Taha and S. J. Malebary, ‘An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine’, IEEE Access, vol. 8, pp. 25579–25587, 2020, doi: 10.1109/ACCESS.2020.2971354.
  • ‘Confusion Matrix - an overview | ScienceDirect Topics’. https://www.sciencedirect.com/topics/engineering/confusion-matrix (accessed Jan. 23, 2023).
  • M. Makhtar, D. C. Neagu, and M. J. Ridley, ‘Comparing multi-class classifiers: On the similarity of confusion matrices for predictive toxicology applications’, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6936 LNCS, pp. 252–261, 2011, doi: 10.1007/978-3-642-23878-9_31.

LabVIEW-based fire extinguisher model based on acoustic airflow vibrations

Year 2023, Volume: 4 Issue: 1, 38 - 47, 25.06.2023

Mahmut Dirik

https://doi.org/10.55195/jscai.1310837

Abstract

In recent years, soundwave-based fire extinguishing systems have emerged as a promising avenue for fire safety measures. Despite this potential, the challenge is to determine the exact operating parameters for efficient performance. To address this gap, we present an artificial intelligence (AI)-enhanced decision support model that aims to improve the effectiveness of soundwave-based fire suppression systems. Our model uses advanced machine learning methods, including artificial neural networks, support vector machines (SVM) and logistic regression, to classify the extinguishing and non-extinguishing states of a flame. The classification is influenced by several input parameters, including the type of fuel, the size of the flame, the decibel level, the frequency, the airflow, and the distance to the flame. Our AI model was developed and implemented in LabVIEW for practical use.
The performance of these machine learning models was thoroughly evaluated using key performance metrics: Accuracy, Precision, Recognition and F1 Score. The results show a superior classification accuracy of 90.893% for the artificial neural network model, closely followed by the logistic regression and SVM models with 86.836% and 86.728% accuracy, respectively. With this study, we highlight the potential of AI in optimizing acoustic fire suppression systems and offer valuable insights for future development and implementation. These insights could lead to a more efficient and effective use of acoustic fire extinguishing systems, potentially revolutionizing the practice of fire safety management

Keywords

Flame Classification Fire Safety Machine Learning Sound Wave-based Fire Extinguishing System

References

  • C. A. Matticks, J. J. Westwater, H. N. Himel, R. F. Morgan, and R. F. Edlich, ‘Health Risks to Fire Fighters’, J Burn Care Rehabil, vol. 13, no. 2, pp. 223–235, Mar. 1992, doi: 10.1097/00004630-199203000-00010.
  • A. B. Morgan and J. W. Gilman, ‘An overview of flame retardancy of polymeric materials: Application, technology, and future directions’, Fire Mater, vol. 37, no. 4, pp. 259–279, Jun. 2013, doi: 10.1002/FAM.2128.
  • M. Shokouhi, K. Nasiriani, H. Khankeh, H. Fallahzadeh, and D. Khorasani-Zavareh, ‘Exploring barriers and challenges in protecting residential fire-related injuries: a qualitative study’, J Inj Violence Res, vol. 11, no. 1, p. 81, 2019, doi: 10.5249/JIVR.V11I1.1059.
  • R. Olawoyin, ‘Nanotechnology: The future of fire safety’, 2018, doi: 10.1016/j.ssci.2018.08.016.
  • Y. Awad, M. Kohail, M. A. Khalaf, and Y. A. Ali, ‘Effect of fire extinguishing techniques on the strength of RC columns’, Asian Journal of Civil Engineering, vol. 23, no. 1, pp. 113–123, Jan. 2022, doi: 10.1007/S42107-021-00414-8/FIGURES/12.
  • F. Dubocq et al., ‘Organic contaminants formed during fire extinguishing using different firefighting methods assessed by nontarget analysis’, Environmental Pollution, vol. 265, p. 114834, Oct. 2020, doi: 10.1016/J.ENVPOL.2020.114834.
  • Y. Zhou, R. Bu, J. Gong, X. Zhang, C. Fan, and X. Wang, ‘Assessment of a clean and efficient fire-extinguishing technique: Continuous and cycling discharge water mist system’, J Clean Prod, vol. 182, pp. 682–693, May 2018, doi: 10.1016/J.JCLEPRO.2018.02.046.
  • M. Rajczyk et al., ‘Application of acoustic oscillations in flame extinction in a presence of obstacle’, J Phys Conf Ser, vol. 1101, no. 1, p. 012023, Oct. 2018, doi: 10.1088/1742-6596/1101/1/012023.
  • A. B. Morgan and J. W. Gilman, ‘An overview of flame retardancy of polymeric materials: application, technology, and future directions’, Fire Mater, vol. 37, no. 4, pp. 259–279, Jun. 2013, doi: 10.1002/FAM.2128.
  • V. Sharifi, A. M. Kempf, and C. Beck, ‘Large-Eddy Simulation of Acoustic Flame Response to High-Frequency Transverse Excitations’, https://doi.org/10.2514/1.J056818, vol. 57, no. 1, pp. 327–340, Nov. 2018, doi: 10.2514/1.J056818.
  • Y. S. Taspinar, M. Koklu, and M. Altin, ‘Acoustic-Driven Airflow Flame Extinguishing System Design and Analysis of Capabilities of Low Frequency in Different Fuels’, Fire Technol, vol. 58, no. 3, pp. 1579–1597, May 2022, doi: 10.1007/S10694-021-01208-9/TABLES/4.
  • A. N. Friedman and S. I. Stoliarov, ‘Acoustic extinction of laminar line-flames’, Fire Saf J, vol. 93, pp. 102–113, Oct. 2017, doi: 10.1016/J.FIRESAF.2017.09.002.
  • X. Shi, Y. Zhang, X. Chen, Y. Zhang, Q. Ma, and G. Lin, ‘The response of an ethanol pool fire to transverse acoustic waves’, Fire Saf J, vol. 125, p. 103416, Oct. 2021, doi: 10.1016/J.FIRESAF.2021.103416.
  • C. Xiong, Y. Liu, C. Xu, and X. Huang, ‘Acoustical Extinction of Flame on Moving Firebrand for the Fire Protection in Wildland–Urban Interface’, Fire Technol, vol. 57, no. 3, pp. 1365–1380, May 2021, doi: 10.1007/S10694-020-01059-W/FIGURES/11.
  • J. O’Connor, V. Acharya, and T. Lieuwen, ‘Transverse combustion instabilities: acoustic, fluid mechanic, and flame processes’, Prog Energy Combust Sci, vol. 49, pp. 1–39, Aug. 2015, doi: 10.1016/j.pecs.2015.01.001.
  • A. N. Friedman and S. I. Stoliarov, ‘Acoustic extinction of laminar line-flames’, Fire Saf J, vol. 93, pp. 102–113, Oct. 2017, doi: 10.1016/j.firesaf.2017.09.002.
  • F. Baillot and F. Lespinasse, ‘Response of a laminar premixed V-flame to a high-frequency transverse acoustic field’, Combust Flame, vol. 161, no. 5, pp. 1247–1267, May 2014, doi: 10.1016/J.COMBUSTFLAME.2013.11.009.
  • E. Beisner et al., ‘Acoustic Flame Suppression Mechanics in a Microgravity Environment’, Microgravity Sci Technol, vol. 27, no. 3, pp. 141–144, Jun. 2015, doi: 10.1007/S12217-015-9422-4/FIGURES/5.
  • T. Y. T. K. M Tunabe, ‘Numerical Simulation on the Flame Propagation in Acoustic Fields’, JASMA, vol. 23, pp. 371–375, 2008.
  • M. Z. Abbasi, P. S. Wilson, and O. A. Ezekoye, ‘Modeling acoustic propagation in a compartment fire’, J Acoust Soc Am, vol. 134, no. 5, pp. 4218–4218, Nov. 2013, doi: 10.1121/1.4831486.
  • M. Z. Abbasi, O. A. Ezekoye, and P. S. Wilson, ‘Measuring the acoustic response of a compartment fire’, Proceedings of Meetings on Acoustics, vol. 19, 2013, doi: 10.1121/1.4799626.
  • M. Z. Abbasi, P. S. Wilson, and O. A. Ezekoye, ‘Change in acoustic impulse response of a room due to a fire’, J Acoust Soc Am, vol. 147, no. 6, p. EL546, Jun. 2020, doi: 10.1121/10.0001415.
  • M. J. Sousa, A. Moutinho, and M. Almeida, ‘Classification of potential fire outbreaks’, Expert Syst Appl, vol. 129, pp. 216–232, Sep. 2019, doi: 10.1016/J.ESWA.2019.03.030.
  • Y. Ye, X. Luo, C. Dong, Y. Xu, and Z. Zhang, ‘Numerical and experimental investigation of soot suppression by acoustic oscillated combustion’, ACS Omega, vol. 5, no. 37, pp. 23866–23875, Sep. 2020, doi: 10.1021/ACSOMEGA.0C03107/SUPPL_FILE/AO0C03107_SI_006.AVI.
  • J. Lloret, M. Garcia, D. Bri, and S. Sendra, ‘A wireless sensor network deployment for rural and forest fire detection and verification’, Sensors, vol. 9, no. 11, pp. 8722–8747, Nov. 2009, doi: 10.3390/S91108722.
  • B. L. Wenning, D. Pesch, A. Timm-Giel, and C. Görg, ‘Environmental monitoring aware routing: Making environmental sensor networks more robust’, Telecommun Syst, vol. 43, no. 1–2, pp. 3–11, Feb. 2010, doi: 10.1007/S11235-009-9191-8.
  • A. A. A. Alkhatib, ‘A Review on Forest Fire Detection Techniques’:, http://dx.doi.org/10.1155/2014/597368, vol. 2014, Mar. 2014, doi: 10.1155/2014/597368.
  • P. Barmpoutis, P. Papaioannou, K. Dimitropoulos, and N. Grammalidis, ‘A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing’, Sensors 2020, Vol. 20, Page 6442, vol. 20, no. 22, p. 6442, Nov. 2020, doi: 10.3390/S20226442.
  • K. Grover, D. Kahali, S. Verma, and B. Subramanian, ‘WSN-Based System for Forest Fire Detection and Mitigation’, pp. 249–260, 2020, doi: 10.1007/978-981-13-7968-0_19.
  • S. J. Chen, D. C. Hovde, K. A. Peterson, and A. W. Marshall, ‘Fire detection using smoke and gas sensors’, Fire Saf J, vol. 42, no. 8, pp. 507–515, Nov. 2007, doi: 10.1016/J.FIRESAF.2007.01.006.
  • G. H. Mitri, I. Z. Gitas, G. H. Mitri, and I. Z. Gitas, ‘Fire type mapping using object-based classification of Ikonos imagery’, Int J Wildland Fire, vol. 15, no. 4, pp. 457–462, Dec. 2006, doi: 10.1071/WF05085.
  • I. Z. Gitas, G. H. Mitri, and G. Ventura, ‘Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery’, Remote Sens Environ, vol. 92, no. 3, pp. 409–413, Aug. 2004, doi: 10.1016/J.RSE.2004.06.006.
  • M. G. Cruz, J. S. Gould, J. J. Hollis, and W. L. McCaw, ‘A Hierarchical Classification of Wildland Fire Fuels for Australian Vegetation Types’, Fire 2018, Vol. 1, Page 13, vol. 1, no. 1, p. 13, Apr. 2018, doi: 10.3390/FIRE1010013.
  • ‘Acoustic Extinguisher Fire Dataset | Kaggle’. https://www.kaggle.com/datasets/muratkokludataset/acoustic-extinguisher-fire-dataset (accessed May 27, 2022).
  • Y. S. Taspinar, M. Koklu, and M. Altin, ‘Classification of flame extinction based on acoustic oscillations using artificial intelligence methods’, Case Studies in Thermal Engineering, vol. 28, Dec. 2021, doi: 10.1016/J.CSITE.2021.101561.
  • Y. S. Taspinar, M. Koklu, and M. Altin, ‘Acoustic-Driven Airflow Flame Extinguishing System Design and Analysis of Capabilities of Low Frequency in Different Fuels’, Fire Technol, May 2022, doi: 10.1007/S10694-021-01208-9.
  • M. Koklu and Y. S. Taspinar, ‘Determining the Extinguishing Status of Fuel Flames with Sound Wave by Machine Learning Methods’, IEEE Access, vol. 9, pp. 86207–86216, 2021, doi: 10.1109/ACCESS.2021.3088612.
  • W. S. McCulloch and W. Pitts, ‘A logical calculus of the ideas immanent in nervous activity’, Bull Math Biophys, vol. 5, no. 4, pp. 115–133, Dec. 1943, doi: 10.1007/BF02478259/METRICS.
  • ‘Hebb, D. O. The organization of behavior: A neuropsychological theory. New York: John Wiley and Sons, Inc., 1949. 335 p. $4.00’, Sci Educ, vol. 34, no. 5, pp. 336–337, Dec. 1950, doi: 10.1002/SCE.37303405110.
  • F. Rosenblatt, ‘The perceptron: A probabilistic model for information storage and organization in the brain’, Psychol Rev, vol. 65, no. 6, pp. 386–408, Nov. 1958, doi: 10.1037/H0042519.
  • C. A. Tudor, ‘Analysis of the Rosenblatt process’, ESAIM: Probability and Statistics, vol. 12, pp. 230–257, Oct. 2008, doi: 10.1051/PS:2007037.
  • A. Shmilovici, ‘Support Vector Machines’, Data Mining and Knowledge Discovery Handbook, pp. 231–247, 2009, doi: 10.1007/978-0-387-09823-4_12.
  • A. v. Joshi, ‘Support Vector Machines’, Machine Learning and Artificial Intelligence, pp. 89–99, 2023, doi: 10.1007/978-3-031-12282-8_8.
  • Ingo. Steinwart and Andreas. Christmann, ‘Support vector machines’, p. 601, 2008.
  • G. Teles, J. J. P. C. Rodrigues, R. A. L. Rabêlo, and S. A. Kozlov, ‘Comparative study of support vector machines and random forests machine learning algorithms on credit operation’, Softw Pract Exp, vol. 51, no. 12, pp. 2492–2500, Dec. 2021, doi: 10.1002/SPE.2842.
  • S. Kim, Z. Yu, R. M. Kil, and M. Lee, ‘Deep learning of support vector machines with class probability output networks’, Neural Networks, vol. 64, pp. 19–28, Apr. 2015, doi: 10.1016/J.NEUNET.2014.09.007.
  • B. Schölkopf, ‘SVMs - A practical consequence of learning theory’, IEEE Intelligent Systems and Their Applications, vol. 13, no. 4, pp. 18–21, Jul. 1998, doi: 10.1109/5254.708428.
  • ‘Support Vector Machines for Regression.’, Support Vector Machines, pp. 330–351, Aug. 2008, doi: 10.1007/978-0-387-77242-4_9.
  • S. Nusinovici et al., ‘Logistic regression was as good as machine learning for predicting major chronic diseases’, J Clin Epidemiol, vol. 122, pp. 56–69, Jun. 2020, doi: 10.1016/J.JCLINEPI.2020.03.002.
  • T. Rymarczyk, E. Kozłowski, G. Kłosowski, and K. Niderla, ‘Logistic Regression for Machine Learning in Process Tomography’, Sensors 2019, Vol. 19, Page 3400, vol. 19, no. 15, p. 3400, Aug. 2019, doi: 10.3390/S19153400.
  • E. Bisong, ‘Logistic Regression’, Building Machine Learning and Deep Learning Models on Google Cloud Platform, pp. 243–250, 2019, doi: 10.1007/978-1-4842-4470-8_20.
  • S. Orozco-Arias, J. S. Piña, R. Tabares-Soto, L. F. Castillo-Ossa, R. Guyot, and G. Isaza, ‘Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements’, doi: 10.3390/pr8060638.
  • S. Adhikari, S.-L. Normand, J. Bloom, D. Shahian, and S. Rose, ‘Revisiting performance metrics for prediction with rare outcomes’, doi: 10.1177/09622802211038754.
  • M. Steurer, R. J. Hill, and N. Pfeifer, ‘Metrics for evaluating the performance of machine learning based automated valuation models’, Journal of Property Research, vol. 38, no. 2, pp. 99–129, 2021, doi: 10.1080/09599916.2020.1858937.
  • A. Rácz, D. Bajusz, and K. Héberger, ‘molecules Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics’, doi: 10.3390/molecules24152811.
  • M. Dirik, ‘Optimized Anfis Model with Hybrid Metaheuristic Algorithms for Facial Emotion Recognition’, International Journal of Fuzzy Systems, pp. 1–12, Oct. 2022, doi: 10.1007/S40815-022-01402-Z/FIGURES/5.
  • D. Chicco and G. Jurman, ‘The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation’, BMC Genomics, vol. 21, no. 1, pp. 6-1-6–13, Jan. 2020, doi: 10.1186/s12864-019-6413-7.
  • D. M. W. Powers, ‘Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation’, Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011, Accessed: Oct. 28, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:55767944#id-name=S2CID
  • T. Fawcett, ‘An Introduction to ROC Analysis’, Pattern Recognit Lett, vol. 27, no. 8, pp. 861–874, Jun. 2006, doi: 10.1016/j.patrec.2005.10.010.
  • D. Chicco and G. Jurman, ‘The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation’, BMC Genomics, vol. 21, no. 1, pp. 6-1-6–13, Jan. 2020, doi: 10.1186/s12864-019-6413-7.
  • S. Josephine Isabella, S. Srinivasan, and G. Suseendran, ‘An Efficient Study of Fraud Detection System Using Ml Techniques’, in Lecture Notes in Networks and Systems, Springer, 2020, pp. 59–67. doi: 10.1007/978-981-15-3284-9_8. [ A. A. Taha and S. J. Malebary, ‘An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine’, IEEE Access, vol. 8, pp. 25579–25587, 2020, doi: 10.1109/ACCESS.2020.2971354.
  • ‘Confusion Matrix - an overview | ScienceDirect Topics’. https://www.sciencedirect.com/topics/engineering/confusion-matrix (accessed Jan. 23, 2023).
  • M. Makhtar, D. C. Neagu, and M. J. Ridley, ‘Comparing multi-class classifiers: On the similarity of confusion matrices for predictive toxicology applications’, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6936 LNCS, pp. 252–261, 2011, doi: 10.1007/978-3-642-23878-9_31.
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Details

Primary Language English
Subjects Computer Software
Journal Section Research Articles
Authors

Mahmut Dirik 0000-0003-1718-5075

Early Pub Date June 30, 2023
Publication Date June 25, 2023
Submission Date June 6, 2023
Published in Issue Year 2023 Volume: 4 Issue: 1

Cite

APA Dirik, M. (2023). LabVIEW-based fire extinguisher model based on acoustic airflow vibrations. Journal of Soft Computing and Artificial Intelligence, 4(1), 38-47. https://doi.org/10.55195/jscai.1310837
AMA Dirik M. LabVIEW-based fire extinguisher model based on acoustic airflow vibrations. JSCAI. June 2023;4(1):38-47. doi:10.55195/jscai.1310837
Chicago Dirik, Mahmut. “LabVIEW-Based Fire Extinguisher Model Based on Acoustic Airflow Vibrations”. Journal of Soft Computing and Artificial Intelligence 4, no. 1 (June 2023): 38-47. https://doi.org/10.55195/jscai.1310837.
EndNote Dirik M (June 1, 2023) LabVIEW-based fire extinguisher model based on acoustic airflow vibrations. Journal of Soft Computing and Artificial Intelligence 4 1 38–47.
IEEE M. Dirik, “LabVIEW-based fire extinguisher model based on acoustic airflow vibrations”, JSCAI, vol. 4, no. 1, pp. 38–47, 2023, doi: 10.55195/jscai.1310837.
ISNAD Dirik, Mahmut. “LabVIEW-Based Fire Extinguisher Model Based on Acoustic Airflow Vibrations”. Journal of Soft Computing and Artificial Intelligence 4/1 (June 2023), 38-47. https://doi.org/10.55195/jscai.1310837.
JAMA Dirik M. LabVIEW-based fire extinguisher model based on acoustic airflow vibrations. JSCAI. 2023;4:38–47.
MLA Dirik, Mahmut. “LabVIEW-Based Fire Extinguisher Model Based on Acoustic Airflow Vibrations”. Journal of Soft Computing and Artificial Intelligence, vol. 4, no. 1, 2023, pp. 38-47, doi:10.55195/jscai.1310837.
Vancouver Dirik M. LabVIEW-based fire extinguisher model based on acoustic airflow vibrations. JSCAI. 2023;4(1):38-47.

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