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@article{205827,
        author = {Ajitkumar Bhondave and Nilesh kakade and Prof. Rahane Divya},
        title = {A Review on Suspicious Activity Prediction based on Deep Learning Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {8644-8647},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205827},
        abstract = {Suspicious activity prediction is vital for enhancing security and surveillance systems by identifying potential threats in real time. This study implements a deep learning-based approach using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze video streams and predict abnormal human behavior. OpenCV is utilized for video processing, while TensorFlow handles model training and inference. The system effectively learns spatial and temporal features to distinguish between normal and suspicious activities. Performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrate that the proposed framework accurately predicts suspicious activities in real time, offering an efficient and reliable solution for automated surveillance applications.},
        keywords = {Deep learning, computer vision, suspicious activity detection, real-time surveillance, human behavior analysis, convolutional neural networks (CNN), activity recognition, anomaly detection, OpenCV, artificial intelligence in security.},
        month = {June},
        }