AUTOMATED ACCIDENTS ON ROAD ANALYSIS: AN OVERVIEW OF STATE OF THE INSIGHTS
- Details
- Hits: 1247
Volume 4 (2), December 2021, Pages 242-262
Mohsin Naseer, Javeria Naz
COMSATS University Islamabad, Wah Camps, Pakistan, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Nowadays, people’s lives are becoming more and more luxurious with the use of technologies. Everyone wants ease and comfort. The trend of having personal vehicles for daily-based usage is increasing rapidly. As more and more people are buying vehicles, the traffic burden is increasing on the roads, causing accidents. When an accident happens, people get injured, and if the emergency services like medical aid are not given on time, then it may cause death. In the upcoming era, the idea of smart cities would be utilized, where every facility and service would be centralized and connected to a server; therefore, devices will be used to send a signal to the nearest emergency response center when an accident is detected on CCTV footage. This work reviews accident and accidental vehicle analysis through automated approaches. The areas of applications are highlighted along with the recent trends and practices discussed in this article.
Keywords:
Accident detection, Road safety, Classification, Review, Smart cities.
DOI: https://doi.org/10.32010/26166127.2021.4.2.242.262
Reference
Abbas, F., et al. (2021). Gender Classification Using Proposed CNN-Based Model and Ant Colony Optimization. Mathematics, 9(19), 2499.
Afzal, A. L., Nair, N. K., & Asharaf, S. (2021). Deep kernel learning in extreme learning machines. Pattern Analysis and Applications, 24(1), 11-19.
Al-Garadi, M. A., et al. (2020). A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Communications Surveys & Tutorials, 22(3), 1646-1685.
Ali, D., et al. (2020). The spectrum of injuries from motorcycle induced road traffic accidents from level one trauma center: A prospective observational study. Chest, 47, 14-1.
Alkinani, M. H., et al. (2022). HSDDD: a hybrid scheme for the detection of distracted driving through fusion of deep learning and handcrafted features. Sensors, 22(5), 1864.
Amin, J., et al. (2020). Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Computing and Applications, 32(20), 15965-15973.
Amin, J., et al. (2020a). Brain tumor detection by using stacked autoencoders in deep learning. Journal of medical systems, 44(2), 1-12.
Amin, J., et al. (2021). 3d semantic deep learning networks for leukemia detection. Computers, Materials & Continua, 69 (1), pp. 785–799.
Amin, J., et al. (2022). Malaria Parasite Detection Using a Quantum-Convolutional Network. Computers, Materials & Continua, 70(3), pp. 6023–6039.
Amin, J., Sharif, M., Raza, M., & Yasmin, M. (2018). Detection of brain tumor based on features fusion and machine learning. Journal of Ambient Intelligence and Humanized Computing, 1-17.
Ao, S. I., Rieger, B. B., & Chen, S. S. (2008). Advances in Computational Algorithms and Data Analysis (Vol. 14). Springer Science & Business Media.
Arróspide, J., Salgado, L., & Nieto, M. (2012). Video analysis-based vehicle detection and tracking using an MCMC sampling framework. EURASIP Journal on Advances in Signal Processing, 2012(1), 1-20.
Ashwini Kumari, P., & Geethanjali, P. (2020). Artificial Neural Network-Based Smart Energy Meter Monitoring and Control Using Global System for Mobile Communication Module. In Soft Computing for Problem Solving (pp. 1-8). Springer, Singapore.
Aung, N. W., & Thein, T. L. L. (2020, February). Vehicle Accident Detection on Highway and Communication to the Closest Rescue Service. In 2020 IEEE Conference on Computer Applications (ICCA) (pp. 1-7). IEEE.
Bangare, P. S., et al. (2019). Survey on accident monitoring system. International Journal of Information and Computing Science, 6(4), 8-18.
Bansal, A., Aggarwal, N., Vij, D., & Sharma, A. (2018, May). An off the shelf CNN features based approach for vehicle classification using acoustics. In International Conference on ISMAC in Computational Vision and Bio-Engineering (pp. 1163-1170). Springer, Cham.
Barros, D., et al. (2020). Machine learning applied to retinal image processing for glaucoma detection: review and perspective. Biomedical engineering online, 19(1), 1-21.
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer vision and image understanding, 110(3), 346-359.
Bhatti, F., Shah, M. A., Maple, C., & Islam, S. U. (2019). A novel internet of things-enabled accident detection and reporting system for smart city environments. Sensors, 19(9), 2071.
Bianchi, F. M., Scardapane, S., Løkse, S., & Jenssen, R. (2020). Reservoir computing approaches for representation and classification of multivariate time series. IEEE transactions on neural networks and learning systems, 32(5), 2169-2179.
Boursianis, A. D., et al. (2020). Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet of Things, 100187.
Cahyani, N., & Muslim, M. A. (2020). Increasing Accuracy of C4. 5 Algorithm by applying discretization and correlation-based feature selection for chronic kidney disease diagnosis. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 12(1), 25-32.
Cano, G., et al. (2017). Automatic selection of molecular descriptors using random forest: Application to drug discovery. Expert Systems with Applications, 72, 151-159.
Chandran, S., Chandrasekar, S., & Elizabeth, N. E. (2016, December). Konnect: An Internet of Things (IoT) based smart helmet for accident detection and notification. In 2016 IEEE Annual India Conference (INDICON) (pp. 1-4). IEEE.
Chang, W. J., et al. (2020). MedGlasses: A wearable smart-glasses-based drug pill recognition system using deep learning for visually impaired chronic patients. IEEE Access, 8, 17013-17024.
Chen, Y., Yu, Y., & Li, T. (2016, August). A vision based traffic accident detection method using extreme learning machine. In 2016 International Conference on Advanced Robotics and Mechatronics (ICARM) (pp. 567-572). IEEE.
Cho, N. C., & Joshi, P. (2020). U.S. Patent No. 10,546,494. Washington, DC: U.S. Patent and Trademark Office.
Chun-Cheng, L., & Goutam, C. (2016). Feature selection based identification of crucial factors for successful advertising on mobile devices. SIG-FPAI, 5(02), 28-32.
Cijun, L., & Yunpeng, L. (2019). Abnormal Driving Behavior Detection Based on Covariance Manifold and LogitBoost. Laser & Optoelectronics Progress, 55(11), 111503.
Das, R., Kumari, K., De, S., Manjhi, P. K., & Thepade, S. (2021). Hybrid descriptor definition for content based image classification using fusion of handcrafted features to convolutional neural network features. International Journal of Information Technology, 13(4), 1365-1374.
Davydov, V., & Bezzateev, S. (2020, January). Accident detection in internet of vehicles using blockchain technology. In 2020 international conference on information networking (ICOIN) (pp. 766-771). IEEE.
Dogru, N., & Subasi, A. (2018, February). Traffic accident detection using random forest classifier. In 2018 15th learning and technology conference (L&T) (pp. 40-45). IEEE.
Engelbrecht, J., Booysen, M. J., van Rooyen, G. J., & Bruwer, F. J. (2015). Survey of smartphone-based sensing in vehicles for intelligent transportation system applications. IET Intelligent Transport Systems, 9(10), 924-935.
Fang, L., Zhang, H., Zhou, J., & Wang, X. (2020). Image classification with an RGB-channel nonsubsampled contourlet transform and a convolutional neural network. Neurocomputing, 396, 266-277.
Fayyaz, A. M., et al. (2022). Leaf Blights Detection and Classification in Large Scale Applications. INTELLIGENT AUTOMATION AND SOFT COMPUTING, 31(1), 507-522.
Fayyaz, M., et al. (2020). Person re-identification with features-based clustering and deep features. Neural Computing and Applications, 32(14), 10519-10540.
Fayyaz, M., Yasmin, M., Sharif, M., & Raza, M. (2021). J-LDFR: joint low-level and deep neural network feature representations for pedestrian gender classification. Neural Computing and Applications, 33(1), 361-391.
Fernandes, B., et al. (2016). Automatic accident detection with multi-modal alert system implementation for ITS. Vehicular Communications, 3, 1-11.
Fernandes, S. L., & Bala, G. J. (2016). Fusion of sparse representation and dictionary matching for identification of humans in uncontrolled environment. Computers in biology and medicine, 76, 215-237.
Geiger, A., Lenz, P., & Urtasun, R. (2012, June). Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3354-3361). IEEE.
Ghosal, A., & Halder, S. (2018). Building intelligent systems for smart cities: issues, challenges and approaches. In Smart cities (pp. 107-125). Springer, Cham.
Hadjidimitriou, N. S., et al. (2019). Machine learning for severity classification of accidents involving powered two wheelers. IEEE Transactions on Intelligent Transportation Systems, 21(10), 4308-4317.
Hai, T., et al. (2020). Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model. IEEE Access, 8, 12026-12042.
Huang, K., et al. (2020). Multiple instance deep learning for weakly-supervised visual object tracking. Signal Processing: Image Communication, 84, 115807.
Huang, X., He, P., Rangarajan, A., & Ranka, S. (2020). Intelligent intersection: Two-stream convolutional networks for real-time near-accident detection in traffic video. ACM Transactions on Spatial Algorithms and Systems (TSAS), 6(2), 1-28.
Hussain Shah, J., et al. (2015). Robust face recognition technique under varying illumination. Journal of applied research and technology, 13(1), 97-105.
Irum, I., Shahid, M. A., Sharif, M., & Raza, M. (2015). A Review of Image Denoising Methods. Journal of Engineering Science & Technology Review, 8(5).
Irum, I., Sharif, M., Raza, M., & Mohsin, S. (2015). A nonlinear hybrid filter for salt & pepper noise removal from color images. Journal of applied research and technology, 13(1), 79-85.
Irum, I., Sharif, M., Raza, M., & Yasmin, M. (2014). Salt and pepper noise removal filter for 8-bit images based on local and global occurrences of Grey levels as selection indicator. Nepal Journal of Science and Technology, 15(2), 123-132.
Irum, I., Sharif, M., Yasmin, M., Raza, M., & Azam, F. (2014). A noise adaptive approach to impulse noise detection and reduction. Nepal Journal of Science and Technology, 15(1), 67-76.
Isola, P., Xiao, J., Torralba, A., & Oliva, A. (2011, June). What makes an image memorable?. In CVPR 2011 (pp. 145-152). IEEE.
Jebril, N.A., et al. (2017). Complete Microcontroller Based Vehicle Accident Detection System with Case Study for Saudi Arabia. WSEAS TRANSACTIONS on COMMUNICATIONS archive, 16.
Jiang, H., Wang, Y., & Yang, Y. (2019, October). Fast Traffic Accident Identification Method Based on SSD Model. In Proceedings of the 2019 International Conference on Robotics Systems and Vehicle Technology (pp. 177-181).
Jing, S., & Yang, L. (2020). A robust extreme learning machine framework for uncertain data classification. The Journal of Supercomputing, 76(4), 2390-2416.
John, A., & Nishanth, P. R. (2017, April). Real time embedded system for accident prevention. In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) (Vol. 2, pp. 645-648). IEEE.
Joshi, D., & Singh, T. P. (2020). A survey of fracture detection techniques in bone X-ray images. Artificial Intelligence Review, 53(6), 4475-4517.
Juliet, S. E., Sadasivam, V., & Florinabel, D. J. (2014). Effective layer-based segmentation of compound images using morphology. Journal of real-time image processing, 9(2), 299-314.
Khalil, U., Javid, T., & Nasir, A. (2017, November). Automatic road accident detection techniques: A brief survey. In 2017 International Symposium on Wireless Systems and Networks (ISWSN) (pp. 1-6). IEEE.
Khaliq, K. A., et al. (2018). Experimental validation of an accident detection and management application in vehicular environment. Computers & Electrical Engineering, 71, 137-150.
Khan, M. A., et al. (2019). Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection. Expert Systems, e12497.
Khan, M. A., et al. (2020). An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection. Multimedia Tools and Applications, 79(25), 18627-18656.
Khan, M. A., et al. (2020). Hand-crafted and deep convolutional neural network features fusion and selection strategy: an application to intelligent human action recognition. Applied Soft Computing, 87, 105986.
Khan, M. A., et al. (2020). Improved strategy for human action recognition; experiencing a cascaded design. IET Image Process., 14(5), 818-829.
Khan, M. A., et al. (2021). Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification. Computers & Electrical Engineering, 90, 106956.
Kukreja, S. L., Löfberg, J., & Brenner, M. J. (2006). A least absolute shrinkage and selection operator (LASSO) for nonlinear system identification. IFAC proceedings volumes, 39(1), 814-819.
Kummitha, R. K. R., & Crutzen, N. (2017). How do we understand smart cities? An evolutionary perspective. Cities, 67, 43-52.
Labib, M. F., et al. (2019, June). Road accident analysis and prediction of accident severity by using machine learning in Bangladesh. In 2019 7th International Conference on Smart Computing & Communications (ICSCC) (pp. 1-5). IEEE.
Lan, J., et al. (2016). Real-time automatic obstacle detection method for traffic surveillance in urban traffic. Journal of Signal Processing Systems, 82(3), 357-371.
Laopracha, N., Sunat, K., & Chiewchanwattana, S. (2019). A novel feature selection in vehicle detection through the selection of dominant patterns of histograms of oriented gradients (DPHOG). IEEE Access, 7, 20894-20919.
Lazarow, J., Lee, K., Shi, K., & Tu, Z. (2020). Learning instance occlusion for panoptic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10720-10729).
Lee, J., Azamfar, M., Singh, J., & Siahpour, S. (2020). Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing. IET Collaborative Intelligent Manufacturing, 2(1), 34-36.
Li, C., Fu, Y., Yu, F. R., Luan, T. H., & Zhang, Y. (2020). Vehicle position correction: A vehicular blockchain networks-based GPS error sharing framework. IEEE Transactions on Intelligent Transportation Systems, 22(2), 898-912.
Li, J., Cheng, H., Guo, H., & Qiu, S. (2018). Survey on artificial intelligence for vehicles. Automotive Innovation, 1(1), 2-14.
Li, S., et al. (2017). Pixel-level image fusion: A survey of the state of the art. information Fusion, 33, 100-112.
Liao, C., Shou, G., Liu, Y., Hu, Y., & Guo, Z. (2017, December). Intelligent traffic accident detection system based on mobile edge computing. In 2017 3rd IEEE International Conference on Computer and Communications (ICCC) (pp. 2110-2115). IEEE.
Lupinska-Dubicka, A., et al. (2020). In-car ecall device for automatic accident detection, passengers counting and alarming. In Transactions on Computational Science XXXV (pp. 36-57). Springer, Berlin, Heidelberg.
Maaloul, B., et al. (2017, June). Adaptive video-based algorithm for accident detection on highways. In 2017 12th IEEE International Symposium on Industrial Embedded Systems (SIES) (pp. 1-6). IEEE.
Makhmutova, A., et al. (2019, October). Intelligent detection of object’s anomalies for road surveilance cameras. In 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) (pp. 0762-0767). IEEE.
Malhi, A. K., Batra, S., & Pannu, H. S. (2020). Security of vehicular ad-hoc networks: A comprehensive survey. Computers & Security, 89, 101664.
Masood, S., et al. (2015). A survey on medical image segmentation. Current Medical Imaging, 11(1), 3-14.
Mondal, P. (2011). A silent Tsunami on Indian road: A comprehensive analysis of epidemiological aspects of road traffic accidents. British Journal of Medicine & Medical Research.
Nasir, I. M., et al. (2022). HAREDNet: A deep learning based architecture for autonomous video surveillance by recognizing human actions. Computers & Electrical Engineering, 99, 107805.
Nasr, E., Kfoury, E., & Khoury, D. (2016, November). An IoT approach to vehicle accident detection, reporting, and navigation. In 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) (pp. 231-236). IEEE.
Naz, J., et al. (2021). Detection and classification of gastrointestinal diseases using machine learning. Current Medical Imaging, 17(4), 479-490.
Naz, J., et al. (2021). Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization. Neural Processing Letters, 1-26.
Naz, M., et al. (2021). From ECG signals to images: a transformation based approach for deep learning. PeerJ Computer Science, 7, e386.
Nisa, M., et al. (2020). Hybrid malware classification method using segmentation-based fractal texture analysis and deep convolution neural network features. Applied Sciences, 10(14), 4966.
Ozbayoglu, M., Kucukayan, G., & Dogdu, E. (2016, December). A real-time autonomous highway accident detection model based on big data processing and computational intelligence. In 2016 IEEE International Conference on Big Data (Big Data) (pp. 1807-1813). IEEE.
Parsa, A. B., et al. (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention, 136, 105405.
Pearson, K. (1895). VII. Note on regression and inheritance in the case of two parents. proceedings of the royal society of London, 58(347-352), 240-242.
Pearson, K. (1900). X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302), 157-175.
Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
Polepally, V., & Shahu Chatrapati, K. (2019). Dragonfly optimization and constraint measure-based load balancing in cloud computing. Cluster Computing, 22(1), 1099-1111.
Prabha, C., Sunitha, R., & Anitha, R. (2014). Automatic vehicle accident detection and messaging system using GSM and GPS modem. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(7), 10723-10727.
Ramzan, M., et al. (2021). Gastrointestinal Tract Infections Classification Using Deep Learning. Computers, Materials & Continua, 69(3), pp. 3239–3257
Rashid, M., et al. (2019). Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features. Multimedia Tools and Applications, 78(12), 15751-15777.
Raza, M., et al. (2017, August). Pedestrian classification by using stacked sparse autoencoders. In 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM) (pp. 37-42). IEEE.
Rehman, M., Sharif, M., & Raza, M. (2016). Shape Features Extraction Method for Content based Image Retrieval. Sindh University Research Journal-SURJ (Science Series), 48(1).
Rosati, U., & Conti, S. (2016). What is a smart city project? An urban model or a corporate business plan?. Procedia-Social and Behavioral Sciences, 223, 968-973.
Saba, T., et al. (2018). Fundus image classification methods for the detection of glaucoma: A review. Microscopy research and technique, 81(10), 1105-1121.
Saba, T., et al. (2021). Categorizing the students’ activities for automated exam proctoring using proposed deep L2-GraftNet CNN network and ASO based feature selection approach. IEEE Access, 9, 47639-47656.
Saba, T., et al. (2021). Suspicious activity recognition using proposed deep L4-branched-ActionNet with entropy coded ant colony system optimization. IEEE Access, 9, 89181-89197.
Sahani, M., Dash, P. K., & Samal, D. (2020). A real-time power quality events recognition using variational mode decomposition and online-sequential extreme learning machine. Measurement, 157, 107597.
Salisu, U. O., et al. (2020). Traffic congestion and intelligent transport system in a fast-growing Nigeria City.
Sanathra, M., et al. (2019). Car accident detection and notification: an analytical survey. International Research Journal of Engineering and Technology, 6(8), 1465-1468.
Selvathi, D., Pavithra, P., & Preethi, T. (2017, June). Intelligent transportation system for accident prevention and detection. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 442-446). IEEE.
Shah, G. A., et al. (2015). A review on image contrast enhancement techniques using histogram equalization. Science International, 27(2).
Shahverdy, M., Fathy, M., Berangi, R., & Sabokrou, M. (2020). Driver behavior detection and classification using deep convolutional neural networks. Expert Systems with Applications, 149, 113240.
Shahzad, A., et al. (2021). Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization. Complex & Intelligent Systems, 1-17.
Sharif, M. I., et al. (2021). A decision support system for multimodal brain tumor classification using deep learning. Complex & Intelligent Systems, 1-14.
Sharif, M., et al. (2019, April). Improved video stabilization using SIFT-log polar technique for unmanned aerial vehicles. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1-7). IEEE.
Sharif, M., et al. (2020). A machine learning method with threshold based parallel feature fusion and feature selection for automated gait recognition. Journal of Organizational and End User Computing (JOEUC), 32(2), 67-92.
Sharif, M., et al. (2020). An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recognition Letters, 129, 150-157.
Sharif, M., et al. (2020). Brain tumor detection based on extreme learning. Neural Computing and Applications, 32(20), 15975-15987.
Sharif, M., Irum, I., Yasmin, M., & Raza, M. (2017). Salt & pepper noise removal from digital color images based on mathematical morphology and fuzzy decision. Nepal Journal of Science and Technology, 18(1), 1-7.
Sharif, M., Mohsin, S., Jamal, M. J., & Raza, M. (2010, July). Illumination normalization preprocessing for face recognition. In 2010 the 2nd conference on environmental science and information application technology (Vol. 2, pp. 44-47). IEEE.
Sheikh, M. S., Liang, J., & Wang, W. (2020). An improved automatic traffic incident detection technique using a vehicle to infrastructure communication. Journal of Advanced Transportation, 2020.
Sulistyaningrum, D. R., et al. (2020, March). Vehicle detection using histogram of oriented gradients and real adaboost. In Journal of Physics: Conference Series (Vol. 1490, No. 1, p. 012001). IOP Publishing.
Thakkar, Y., et al. (2020). Spot a Spot—Efficient Parking System Using Single-Shot MultiBox Detector. In Data Engineering and Communication Technology (pp. 931-939). Springer, Singapore.
Tuama, A., Abdulrahman, H., & Magnier, B. (2020, January). Integrated convolutional neural network model with statistical moments layer for vehicle classification. In Twelfth International Conference on Machine Vision (ICMV 2019) (Vol. 11433, pp. 579-584). SPIE.
Tuncer, T., & Dogan, S. (2020). Pyramid and multi kernel based local binary pattern for texture recognition. Journal of Ambient Intelligence and Humanized Computing, 11(3), 1241-1252.
ur Rehman, S., Chen, Z., Shah, J. H., & Raza, M. (2016, July). Multi-feature fusion based re-ranking for person re-identification. In 2016 International Conference on Audio, Language and Image Processing (ICALIP) (pp. 213-216). IEEE.
Van Nguyen, T., et al. (2020). Motorsafe: An android application for motorcyclists using decision tree algorithm. International Journal of Interactive Mobile Technologies, 14(2), 119-128.
Wang, C., Dai, Y., Zhou, W., & Geng, Y. (2020). A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition. Journal of advanced transportation, 2020.
Wang, S. Y., et al. (2020). Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8695-8704).
Wu, L., et al. (2020). Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Computers and Electronics in Agriculture, 168, 105115.
Xiao, W., et al. (2020). Exploring Red, Green, and Blue Light‐Activated Degradation of Perovskite Films and Solar Cells for Near Space Applications. Solar RRL, 4(3), 1900394.
Yang, L., Luo, P., Change Loy, C., & Tang, X. (2015). A large-scale car dataset for fine-grained categorization and verification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3973-3981).
Yu, B., et al. (2020). SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics, 36(4), 1074-1081.
Yue, M., Fan, L., & Shahabi, C. (2018, June). Traffic accident detection with spatiotemporal impact measurement. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 471-482). Springer, Cham.
Zahid, M., et al. (2021). Pedestrian identification using motion-controlled deep neural network in real-time visual surveillance. Soft Computing, 1-17.
Zeng, Z., et al. (2020). Illumination-adaptive person re-identification. IEEE Transactions on Multimedia, 22(12), 3064-3074.
Zhang, W., Han, D., Li, K. C., & Massetto, F. I. (2020). Wireless sensor network intrusion detection system based on MK-ELM. Soft Computing, 24(16), 12361-12374.
Zhao, H., Yu, H., Li, D., Mao, T., & Zhu, H. (2019). Vehicle accident risk prediction based on AdaBoost-so in vanets. IEEE Access, 7, 14549-14557.
Zhao, L., & Li, S. (2020). Object detection algorithm based on improved YOLOv3. Electronics, 9(3), 537.
Zhao, Y., Hao, K., He, H., Tang, X., & Wei, B. (2020). A visual long-short-term memory based integrated CNN model for fabric defect image classification. Neurocomputing, 380, 259-270.