References
- Amin, J., Anjum, M.A., Ibrar, K., Sharif, M., Kadry, S. and Crespo, R.G. (2023). ‘Detection of anomaly in surveillance videos using quantum convolutional neural networks’. Image and Vision Computing, 135, p.104710.
- Ullah, W., Hussain, T., Ullah, F.U.M., Lee, M.Y. and Baik, S.W. (2023). ‘Transcnn: hybrid CNN and transformer mechanism for surveillance anomaly detection’. Engineering Applications of Artificial Intelligence, p.106173.
- De Donato, L., Marrone, S., Flammini, F., Sansone, C., Vittorini, V., Nardone, R., Mazzariello, C. and Bernaudin, F. (2023). ‘Intelligent detection of warning bells at level crossings through deep transfer learning for smarter railway maintenance’. Engineering Applications of Artificial Intelligence, p.106405.
- Yasin, A., Tahir, S.B. and Frnda, J. (2023). ‘Anomaly prediction over human crowded scenes via associate-based data mining and k-ary tree hashing’. International Journal of Intelligent Systems, Article ID 9822428, pp.1–12.
- Choudhry, N., Abawajy, J., Huda, S. and Rao, I. (2023). ‘A comprehensive survey of machine learning methods for surveillance videos anomaly detection’. IEEE Access, 11, p.114680.
- Liu, N. (2024). ‘CCTV cameras at home: temporality experience of surveillance technology in family life’. Journal of New Media & Society, pp.1–19.
- Kaur, N., Rani, S. and Kaur, S. (2024). ‘Real-time video surveillance based human fall detection system using hybrid haar cascade classifier’. Multimedia Tools and Applications, 1, pp.1–3.
- Sengönül, E., Samet, R., Abu Al-Haija, Q., Alqahtani, A., Alturki, B. and Alsulami, A.A. (2023). ‘An analysis of artificial intelligence techniques in surveillance video anomaly detection: a comprehensive survey’. Applied Sciences, 13, p.4956.
- Pelvan, S. Ö., Can, B., and Ozkan, H. (2023). ‘A hierarchical approach for improved anomaly detection in video surveillance’. IEEE Access, 11, pp.1–14.
- Jiang, R., Yang, Z. and Zhao, J. (2023). ‘A complete deep support vector data description for one class learning’. IEEE Access, 11, pp.114688–114694.
- Jemili, F., Meddeb, R. and Kamel, Y. (2023). ‘A comparative study between ensemble learning techniques in intrusion detection context’. Journal of Information Assurance and Security, 18, pp.1–12.
- Cao, Z. and Huang, S.-H.S. (2023). ‘A behavioral graph model for host-based intrusion detection’. Journal of Information Assurance and Security, 18, pp.48–57.
- Hu, W., Hu, T., Wei, Y., Lou, J. and Wang, S. (2023). ‘Global plus local jointly regularized support vector data description for novelty detection’. IEEE Transactions on Neural Networks and Learning Systems, 34, pp.3756–3769.
- Rubaidi, Z.S., Ben Ammar, B. and Ben Aouicha, M. (2023). ‘Vehicle insurance fraud detection based on hybrid approach for data augmentation’. Journal of Information Assurance and Security, 18, pp.135–146.
- Dang, Q.-V. (2023). ‘Conformal prediction in the intrusion detection problem’. Journal of Information Assurance and Security, 18, pp.13–24.
- Cheng, X., Li, X. and Ma, X. (2023). ‘A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window’. Energy Science & Engineering, 11, pp.4493–4504.
- Pragash, K. and Jayabharathy, J. (2023). ‘Relevant subset computation using mutually dependent features and normalized divergence isolation forest using bio-image of heart to classify coronary heart disease’. Analog Integrated Circuits and Signal Processing, 1, pp.1–3.
- Breunig, M.M., Kriegel, H.-P., Ng, R.T. and Sander, J. (2000). ‘LOF: identifying density-based local outliers’. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, ACM, pp.93–104.
- Zhang, X., Wu, J. and Yuan, H. (2021). ‘A clustering-based LOF algorithm for outlier detection in big data’. Journal of Big Data, 8, p.40.
- Singh, N., Kumar, A. and Goyal, S. (2021). ‘Enhanced outlier detection using a hybrid approach of LOF and k-means clustering’. Computers & Electrical Engineering, 98, p.106984.
- Nawaz, A., Khan, S.S. and Ahmad, A. (2024). ‘Ensemble of autoencoders for anomaly detection in biomedical data: a narrative review’. IEEE Access, 12, p.3360691.
- Guo, J., Lu, S., Jia, L., Zhang, W. and Li, H. (2024). ‘Encoder-decoder contrast for unsupervised anomaly detection in medical images’. IEEE Transactions on Medical Imaging, 43, pp.1767–1778.
- Wu, Z., Paoletti, M.E., Su, H. and Tao, X. (2023). ‘Background-guided deformable convolutional autoencoder for hyperspectral anomaly detection’. IEEE Transactions on Geoscience and Remote Sensing, 61, pp.3341–3354.
- Rao, B.C., Raju, K., Ramesh Babu, G. and Pittala, C.S. (2023). ‘An improved Gabor wavelet transform and rough k-means clustering algorithm for MRI brain tumor image segmentation’. Multimedia Tools and Applications, 82, p.28143.
- Thiyagarajan, S.K. and Murugan, K. (2023). ‘Arithmetic optimization-based k means algorithm for segmentation of ischemic stroke lesion’. Soft Computing, pp.1–14.
- Lu, H., Xu, H., Wang, Q., Gao, Q., Yang, M. and Gao, X. (2024). ‘Efficient multi-view k-means for image clustering’. IEEE Transactions on Image Processing, 33, pp.851–862.
- Liu, L., Delnevo, G. and Mirri, S. (2023). ‘Unsupervised hyperspectral image segmentation of films: a hierarchical clustering-based approach’. Journal of Big Data, 10, p.31.
- Lai, M., Cao, L., Lu, H., Ha, Q., Li, L., Hossain, J. and Kennedy, P. (2023). ‘An unsupervised hierarchical clustering approach to improve Hopfield retrieval accuracy’. In: 2023 International Joint Conference on Neural Networks (IJCNN), pp.1–6.
- Sadhukhan, P., Halder, L. and Palit, S. (2024). ‘Approximate DBSCAN on obfuscated data’. Journal of Information Security and Applications, 80, p.103664.
- Panić, B., Nagode, M., Klemenc, J. and Oman, S. (2023). ‘Combining color and spatial image features for unsupervised image segmentation with mixture modelling and spectral clustering’. Mathematics, 11, p.4800.
- Zhou, G.Q., Hua, S.H., He, Y., Wang, K.N., Zhou, D. and Wang, H. (2023). ‘Automatic myotendinous junction identification in ultrasound images based on junction-based template measurements’. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, pp.851–860.
- El Khattabi, M.-Z., El Jai, M., Lahmadi, Y., Oughdir, L. and Rahhali, M. (2023). ‘Understanding the interplay between metrics, normalization forms, and data distribution in k-means clustering: a comparative simulation study’. Computer Engineering and Computer Science, 66(4), pp.943–953.
- He, X., He, F., Fan, Y., Jiang, L., Liu, R. and Maalla, A. (2023). ‘An effective clustering scheme for high-dimensional data’. Multimedia Tools and Applications, 82(3), pp.1–13.
- D’Agostino, D., Diez, M., Felli, M. and Serani, A. (2023). ‘PIV snapshot clustering reveals the dual deterministic and chaotic nature of propeller wakes at macro- and micro-scales’. Journal of Marine Science and Engineering, 11(6), p.1220.
- Chen, Y., Debnath, T., Cai, A. and Song, M. (2023). ‘Circular silhouette and a fast algorithm’. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), pp.1–1.
- Rachwał, A., Popławska, E., Gorgol, I., Cieplak, T., Pliszczuk, D., Skowron, Ł. and Rymarczyk, T. (2023). ‘Determining the quality of a dataset in clustering terms’. Applied Sciences, 13, p.2942.
- Rykov, A., Cordeiro de Amorim, R., Makarenkov, V. and Mirkin, B. (2024). ‘Inertia-based indices to determine the number of clusters in k-means: an experimental evaluation’. IEEE Access, 12.
- Wu, M. I., and Stirling, L. (2024). ‘Emergent gait strategies defined by cluster analysis when using imperfect exoskeleton algorithms’. IEEE Robotics and Automation Letters, 9(4), pp.3171–3178.
- Nikiforova, O., Romanovs, A., Zabiniako, V., and Kornienko, J. (2024). ‘Detecting and identifying insider threats based on advanced clustering methods’. IEEE Access, 12, pp.1–10.
- Fan, Z., Xia, W., Liu, X. and Li, H. (2021). ‘Detection and segmentation of underwater objects from forward-looking sonar based on a modified mask RCNN’. Signal, Image and Video Processing, 15, pp.1135–1143.
- Mazarbhuiya, F.A. and Shenify, M. (2023). ‘A mixed clustering approach for real-time anomaly detection’. Applied Sciences, 13(7), p.4151.
- Rusia, M.K. and Singh, D.K. (2023). ‘A comprehensive survey on techniques to handle face identity threats: challenges and opportunities’. Multimedia Tools and Applications, 82, pp.1669–1748.
- Radanliev, P. and De Roure, D. (2023). ‘New and emerging forms of data and technologies: literature and bibliometric review’. Multimedia Tools and Applications, 82, pp.2887–2911.
- Nuhu, A.A., Zeeshan, Q., Safaei, B. and Shahzad, M.A. (2023). ‘Machine learning-based techniques for fault diagnosis in the semiconductor manufacturing process: a comparative study’. The Journal of Supercomputing.
- Alahakoon, D., Nawaratne, R., Xu, Y., De Silva, D., Sivarajah, U. and Gupta, B. (2023). ‘Self-building artificial intelligence and machine learning to empower big data analytics in smart cities’. Information Systems Frontiers, 25, pp.221–240.
- Mahum, R., Irtaza, A., Nawaz, M., Nazir, T., Masood, M., Shaikh, S. and Abouel Nasr, E. (2023). ‘A robust framework to generate surveillance video summaries using combination of Zernike moments and R-transform and deep neural network’. Multimedia Tools and Applications.
- Dhanhani, A., Damiani, E., Mizouni, R., Wang, D. and Al-Rubaie, A. (2023). ‘Multi-modal traffic event detection using shapelets’. Neural Computing and Applications, 35, pp.1395–1408.
- Ansari, M.A. and Singh, D.K. (2023). ‘Identifying human activities in megastores through postural data to monitor shoplifting events’. Neural Computing and Applications.
- Dangut, M.D., Jennions, I.K., King, S. and Skaf, Z. (2023). ‘A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach’. Neural Computing and Applications, 35, pp.2991–3009.
- Datta, S., Mali, K., Ghosh, U., Bose, S., Das, S. and Ghosh, S. (2023). ‘Rare correlated coherent association rule mining with CLS-MMS’. The Computer Journal, 66.