Land Weber Iterative Supervised Classification and Quantized Spiking Network for Crime Detection Emotion Analysis

Authors

  • M.Jayakandan, A. Chandrabose

Keywords:

Crime Detection, Emotion Analysis, Machine Learning, Image Classification, Pattern Recognition, Neural Networks, Information Security, Supervised Classification

Abstract

Emotion analysis is a promising tool for crime detection. It can identify potential suspects, assess the risk of violence, and track the progress of a criminal investigation. However, it cannot be easy to identify emotions accurately, and several factors can influence the results of emotional analysis. This paper proposes a new approach to emotion analysis in crime detection that utilizes Land Weber iterative supervised classification and quantized spiking network. Land Weber’s iterative supervised classification is a technique that can improve the accuracy of emotion analysis by iteratively training a classifier on a dataset of labeled data. A quantized spiking network is a type of neural network well-suited for emotion analysis because it can capture the temporal dynamics of emotions.

The proposed approach was evaluated on a dataset of facial expressions and voice recordings. The results showed that the proposed approach achieved state-of-the-art accuracy in emotion analysis. The proposed approach has several advantages over traditional approaches to emotion analysis. First, it is more accurate. Second, it is more robust to noise. Third, it is more efficient. The proposed approach can improve the accuracy of emotion analysis in crime detection. It can also be used to develop new applications for emotion analysis, such as a system that automatically detect signs of deception in a witness statement. The Land-Weber iterative supervised classification algorithm has been used to detect emotions in crime detection. The quantized spiking neural network has been used to classify emotions. The study results showed that the Land-Weber iterative supervised classification algorithm achieved % overall accuracy of 97.5% in classifying emotions. In comparison, the quantized spiking neural network achieved an overall accuracy of 97.2%.

Downloads

Download data is not yet available.

References

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). Based on dynamic temporal attention, a quantized spiking neural network for crime scene video analysis. 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.

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.

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).

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.

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).

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.

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.

Rajkumar, V., and V. Maniraj. "Dependency Aware Caching (Dac) For Software Defined Networks." Webology (ISSN: 1735-188X) 18.5 (2021).

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.

**Yang, Z., Zhang, H., and Liu, Y. (2020). I quantized spiking neural network for crime scene video analysis based on multi-level attention. Neurocomputing, 395, 2

Downloads

Published

26.03.2024

How to Cite

M.Jayakandan. (2024). Land Weber Iterative Supervised Classification and Quantized Spiking Network for Crime Detection Emotion Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2219–2224. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5820

Issue

Section

Research Article

Similar Articles

You may also start an advanced similarity search for this article.