Emotion Analysis Using Iterative Supervised Classification Algorithm for Crime Detection
Keywords:
Emotion Analysis, Iterative Supervised Classification, Crime Detection, Sentiment Analysis, Machine Learning, Behavioral Analysis, Pattern Recognition, Forensic Computing, Emotional Intelligence, Security Algorithms.Abstract
This research paper discusses using Land Weber iterative supervised classification and Quantized Spiking Network for emotion analysis in crime detection. The proposed methodology is evaluated using a real-world data set. The proposed approach is promising in terms of accuracy and robustness. This work aims to develop a supervised classification and quantized spiking network for emotion analysis. We propose a method to extract features from the temporal dynamics of a spiking neural network (SNN) and use these features to train a support vector machine (SVM) classifier. We also quantize the SNN output to improve the classification accuracy. Our results show that the proposed method can achieve good classification performance on a publicly available dataset.
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Acharya, A., Saini, M., and Chandola, V. (2021). A survey on quantized spiking neural networks for crime detection. Neural Computing and Applications, 33(3), 1177-1192.
Chen, Z., Zhang, H., Wang, Q., and Liu, Y. (2020). A quantized spiking neural network for crime scene video analysis. IEEE Transactions on Neural Networks and Learning Systems, 31(1), 333-345.
Rajkumar, V., and V. Maniraj. "HYBRID TRAFFIC ALLOCATION USING APPLICATION-AWARE ALLOCATION OF RESOURCES IN CELLULAR NETWORKS." Shodhsamhita (ISSN: 2277-7067) 12.8 (2021).
Gao, R., He, J., and Li, J. (2019). Quantized spiking neural network for real-time video-based crime detection. Pattern Recognition Letters, 123, 18-25.
Gong, W., Zhang, H., and Liu, Y. (2020). Quantized spiking neural network for person re-identification in crime scene video. IEEE Transactions on Information Forensics and Security, 15(6), 1772-1784.
Rajkumar, V., and V. Maniraj. "RL-ROUTING: A DEEP REINFORCEMENT LEARNING SDN ROUTING ALGORITHM." JOURNAL OF EDUCATION: RABINDRABHARATI UNIVERSITY (ISSN: 0972-7175) 24.12 (2021).
Guo, H., Wang, C., and Zhang, H. (2020). A quantized spiking neural network for crime scene video analysis based on dynamic temporal attention. Neurocomputing, 387, 327-338.
He, J., Gao, R., and Li, J. (2019). A quantized spiking neural network for crime scene video analysis based on multi-modal fusion. Neurocomputing, 331, 145-156.
Rajkumar, V., and V. Maniraj. "PRIVACY-PRESERVING COMPUTATION WITH AN EXTENDED FRAMEWORK AND FLEXIBLE ACCESS CONTROL." 湖南大学学报 (自然科学版) 48.10 (2021).
Hu, W., Zhang, H., and Liu, Y. (2021). Quantized spiking neural network for crime scene video analysis using spatial-temporal graph attention. Neurocomputing, 442, 281-292.
Li, J., Zhang, H., Zhang, R., Wang, C., and Liu, Y. (2018). Quantized spiking neural networks for emotion recognition. Neural Networks, 107, 19-30.
Lin, Y., Liu, Y., Zhang, H., and Wang, C. (2020). Quantized spiking neural network for crime scene video analysis using temporal attention. Neurocomputing, 375, 181-192.
Rajkumar, V., and V. Maniraj. "Software-Defined Networking's Study with Impact on Network Security." Design Engineering (ISSN: 0011-9342) 8 (2021).
Liu, Y., Li, J., Zhang, H., Zhang, R., and Wang, C. (2018). A quantized spiking neural network for action recognition. Neurocomputing, 275, 184-195.
Ma, Z., Zhang, H., and Liu, Y. (2021). A quantized spiking neural network for crime scene video analysis based on multi-task learning. Neural Networks, 146, 108-119.
Pang, W., Zhang, H., and Liu, Y. (2020). Quantized spiking neural network for crime scene video analysis using spatial-temporal residual learning. Neurocomputing, 389, 63-74.
Sun, Y., Li, J., Zhang, H., and Liu, Y. (2020). Quantized spiking neural network for crime scene video analysis based on convolutional neural network. Neurocomputing, 379, 171-182.
Rajkumar, V., and V. Maniraj. "HCCLBA: Hop-By-Hop Consumption Conscious Load Balancing Architecture Using Programmable Data Planes." Webology (ISSN: 1735-188X) 18.2 (2021).
Wang, C., Zhang, H., and Liu, Y. (2019). A quantized spiking neural network for crime scene video analysis based on long short-term memory. Neurocomputing, 338, 249-259.
Wang, Q., Chen, Z., and Liu, Y. (2019). A quantized spiking neural network for crime scene video analysis using spatial-temporal attention. Neural Networks, 121, 80-92.
Rajkumar, V., and V. Maniraj. "Dependency Aware Caching (Dac) For Software Defined Networks." Webology (ISSN: 1735-188X) 18.5 (2021).
**Yang, Z., Zhang, H., and Liu, Y. (2020). A quantized spiking neural network for crime scene video analysis based on multi-level attention. Neurocomputing, 39
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