Analysis neutral network for smoke detection predictions
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Published:
Feb 28, 2024
Keywords:
Accuracy, Fire, Neural Network, Prediction, Smoke
Section
Computer Science
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Abstract
The applications of neural networks is smoke detection, which is the task of identifying whether an image contains smoke or not. Smoke detection is important for industrial safety, fire prevention, and environmental monitoring. However, smoke detection is challenging because smoke can have different shapes, textures, colors, and intensities depending on the source, environment, and lighting conditions, Smoke detection is important for protecting lives, properties, and the environment from the damages caused by fire. Smoke detection can also be used for monitoring and controlling industrial processes, such as combustion, welding, or smelting. Thermal smoke sensors use a thermistor or a thermocouple to measure the temperature change caused by smoke. Gas smoke sensors use a semiconductor or an electrochemical cell to measure the concentration of gas molecules produced by smoke. Neural networks can also predict the spread and evolution of forest fires, as well as help decision makers plan mitigation methods and extinguishing tactics.
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How to Cite
Sapriadi, S. (2024) “Analysis neutral network for smoke detection predictions”, Jurnal Mantik, 7(4), pp. 3488-3494. doi: 10.35335/mantik.v7i4.4514.
References
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H. Mayatopani, R. I. Borman, W. T. Atmojo, and A. Arisantoso. (2021). Classification of Vehicle Types Using Backpropagation Neural Networks with Metric and Ecentricity Parameters. J. Ris. Inform., vol. 4, no. 1, pp. 65–70, doi: 10.34288/jri.v4i1.293.
Herdiansah, R. I. Borman, D. Nurnaningsih, A. A. J. Sinlae, and R. R. Al Hakim. (2022). Klasifikasi Citra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk. JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 2, pp. 388–395. doi: 10.30865/jurikom.v9i1.3846.
Izhari, F., & Dhany, H. W. (2020). Comparison of Air Quality Data Accuration Using Decision Tree And Neural Network Method. Jurnal Ipteks Terapan, 14(2), 123-127
Jinkyu Ryu and Dongkurl Kwak. (2022). A Study on a Complex Flame and Smoke Detection Method Using Computer Vision Detection and Convolutional Neural Network. Graduate School of Disaster Prevention, Kangwon National University, Samcheok 25913, Korea.
K. Saputra S and M. I. Perangin-Angin. (2020). Klasifikasi Tanaman Obat Berdasarkan Ekstraksi Fitur Morfologi Daun Menggunakan Jaringan Syaraf Tiruan. J. Inform., vol. 5, no. 2, pp. 169–174, 2018, doi: 10.31311/ji.v5i2.3770.
Kamal Ali Abu Al-Reesh, Muhammad Nidal Al-Safadi, Waleed Sami Al-tanani and Samy S. Abu-Naser. (2023). Predictive Modeling of Smoke Potential Using Neural Networks and Environmental Data. International Journal of Engineering and Information Systems (IJEAIS)
Putra, P. H., Zarlis, M., & Mawengkang, H. (2020). Analysis of the effect number input and hidden layer variations on the addition kohonen algorithm to backpropagation method. In IOP Conference Series: Materials Science and Engineering (Vol. 725, No. 1, p. 012096). IOP Publishing.
R. I. Borman, I. Ahmad, and Y. Rahmanto. (2022). Klasifikasi Citra Tanaman Perdu Liar Berkhasiat Obat Menggunakan Jaringan Syaraf Tiruan Radial Basis Function. Bull. Informatics Data Sci., vol. 1, no. 1, pp. 6–13.
S. Devella, Y. Yohannes, and F. N. Rahmawati. (2020). Implementasi Random Forest Untuk Klasifikasi Motif Songket Palembang Berdasarkan SIFT,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 2, pp. 310–320.
S. Srivastava. (2018). Weka: A Tool for Data preprocessing, Classification, Ensemble, Clustering and Association Rule Mining. Int. J. Comput. Appl., vol. 88, no. 10, pp. 26–29.
Saru, S. and Subashree, S., (2019). Analysis and prediction of diabetes using machine learning. International journal of emerging technology and innovative engineering, Volume no 5, Issue no 4
Sarwar, M.A., Kamal, N., Hamid, W. and Shah, M.A. (2018). Prediction of diabetes using machine learning algorithms in healthcare. In 2018 24th international conference on automation and computing (ICAC) IEEE, Page no 1-6
Saydirasulov Norkobil Saydirasulovich, Mukhriddin Mukhiddinov, Oybek Djuraev, Akmalbek Abdusalomov, and Young-Im Cho. (2023). An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images. Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea.
Shuhong Cheng. Jiyong Ma. (2019). Smoke detection and trend prediction method based on Deeplabv3+ and generative adversarial network. Journal of Electronic Imaging.
Veerappampalayam Easwaramoorthy Sathishkumar, Jaehyuk Cho, Malliga Subramanian and Obuli Sai Naren. (2023). Forest fre and smoke detection using deep learning-based learning without forgetting. Fire Ecology.
Vergni, Lorenzo., Todisco, Francesca. (2023). A Random Forest Machine Learning Approach for the Identification and Quantification of Erosive Events. Department of Agricultural, Food and Environmental Science, University of Perugia, 06124 Perugia, Italy.
Wilkinson, C., Ferris, F.L., Klein, R.E., Lee, P.P., Agardh, C.D., Davis, M., Dills, D., Kampik, A., Pararajasegaram, R., Verdaguer, J.T., et al. (2003). Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 110, 1677–1682.
Wu, H., Yang, S., Huang, Z., He, J. and Wang, X. (2018). Type 2 diabetes mellitus prediction model based on data mining. Informatics in Medicine Unlocked, 10, pp.100-107.
Yihong Zhang, Qin Lin, Changshuai Qin, Hang Ge. (2022). Forest Fire Smoke Detection Method Based on MoAm-YOLOv4 Algorithm. Journal of Computer and Communications.
Z. Abidin, R. I. Borman, F. B. Ananda, P. Prasetyawan, F. Rossi, and Y. Jusman. (2022). Classification of Indonesian Traditional Snacks Based on Image Using Convolutional Neural Network (CNN) Algorithm. in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS). pp. 18–23.
Z. Azmi. (2021). Artificial Neural Network Model For Wind Mill. Int. J. Eng. Sci. InformationTechnology, vol. 1, no. 3, pp. 40–48.
Z. Sabouri, Y. Maleh, and N. Gherabi, (2022). Benchmarking Classification Algorithms for Measuring the Performance on Maintainable Applications. in Advances in Information, Communication and Cybersecurity, Cham, pp. 173–179. doi: 10.1007/978-3-030-91738-8_17
Zidian Xie, Olga Nikolayeva, MS, Jiebo Luo and Dongmei Li. (2019). Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques. National Centre for Biotechnology information, Volume no 50, Page no 100-105
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y. and Tang, H. (2018). Predicting diabetes mellitus with machine learning techniques. Frontiers in genetics, Volume no 9, page no 115.
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