Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed. This change increases the demand for automated systems that detect abnormal events or anomalies, such as road accidents, fighting, snatching, car fires, and explosions in real-time. These systems improve detection accuracy, minimize human error, and make security operations more efficient. In this study, we proposed the Composite Recurrent Bi-Attention (CRBA) model for detecting anomalies in surveillance videos. The CRBA model combines DenseNet201 for robust spatial feature extraction with BiLSTM networks that capture temporal dependencies across video frames. A multi-attention mechanism was also incorporated to direct the model’s focus to critical spatiotemporal regions. This improves the system’s ability to distinguish between normal and abnormal behaviors. By integrating these methodologies, the CRBA model improves the detection and classification of anomalies in surveillance videos, effectively addressing both spatial and temporal challenges. Experimental assessments demonstrate that the CRBA model achieves high accuracy on both the University of Central Florida (UCF) and the newly developed Road Anomaly Dataset (RAD). This model enhances detection accuracy while also improving resource efficiency and minimizing response times in critical situations. These advantages make it an invaluable tool for public safety and security operations, where rapid and accurate responses are needed for maintaining safety.

Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person’s life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient’s survival. Due to the different characteristics and data limitations of brain tumors is challenging problems to classify the three different types of brain tumors. A convolutional neural networks (CNNs) learning algorithm integrated with data augmentation techniques was used to improve the model performance. CNNs have been extensively utilized in identifying brain tumors through the analysis of Magnetic Resonance Imaging (MRI) images The primary aim of this research is to propose a novel method that achieves exceptionally high accuracy in classifying the three distinct types of brain tumors. This paper proposed a novel Stack Ensemble Transfer Learning model called “SETL_BMRI”, which can recognize brain tumors in MRI images with elevated accuracy. The SETL_BMRI model incorporates two pre-trained models, AlexNet and VGG19, to improve its ability to generalize. Stacking combined outputs from these models significantly improved the accuracy of brain tumor detection as compared to individual models. The model’s effectiveness is evaluated using a public brain MRI dataset available on Kaggle, containing images of three types of brain tumors (meningioma, glioma, and pituitary). The experimental findings showcase the robustness of the SETL_BMRI model, achieving an overall classification accuracy of 98.70%. Additionally, it delivers an average precision, recall, and F1-score of 98.75%, 98.6%, and 98.75%, respectively. The evaluation metric values of the proposed solution indicate that it effectively contributed to previous research in terms of achieving high detection accuracy.

Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are widely used to surveillance normal and anomalous incidents. Real-world anomaly detection in complex environment situations is a significant challenge due to its diverse nature manually reviewing this vast data is difficult, time-consuming, and prone to errors. Human operators may overlook crucial information due to failure, which reduces the accuracy of manual monitoring. This highlights the need for automated solutions to improve accuracy and efficiency We proposed a Bidirectional Motion Temporal (BiMT) model to address this issue by coupling a pre-trained model of deep neural networks. This system utilizes a Convolutional Neural Network (CNN) to extract the best spatial key features from the video stream, which are converted into time series data and coupled with Recurrent Neural Network (RNN) deep learning model Bidirectional Long Short-Term Memory (BiLSTM) extract temporal key features, and Multi-Head Self-Attention (MHSA) allows for the detection of short-term frame correlations. In addition, created a custom transformer encoder with relative positional embedding that improves the detection and recognizes long-range frame dependencies. Furthermore, categorical focal loss (CFL) is used during training to prioritize essential features and further enhance the model's precision. The more sophisticated ConvNeXtv2 model produced average AUC scores of 98.6%, 86.5%, and 83.5% for the UCF-Crime, UBI-Fight, and RAD datasets respectively.

Anomaly detection identifies objects or events that do not behave as expected or correlate with other data points. Anomaly detection has been used to identify and investigate abnormal data components. Detecting anomalous activities is challenging due to insufficient data size of anomalous reality, ground training data, factors related to differences in environmental conditions, working position of capturing cameras, and illumination situations. Anomaly detection has enormous applications that include (but not limited to) industrial damage prevention, sensor network, health-care services, traffic surveillance, and violence prediction. Machine learning techniques, particularly deep learning has enabled tremendous advancements in the area of anomaly detection. In this paper, we sort out an all-inclusive review of the up-to-date research on anomaly detection techniques. We seek to serve as an extensive and comprehensive review of machine and deep learning anomaly detection techniques throughout the foregoing three years 2019-2021. Particularly, we discuss both machine learning and deep learning anomaly detection applications, performance measurements, and anomaly detection classification. We also point out various datasets that have been applied in anomaly detection along with some fairly new real-world datasets. Finally, we investigate current challenges and future research prospects in this area.

Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are used to surveillance and monitor the normal and anomalous incidents. Real-world anomaly detection is a significant challenge due to its complex and diverse nature. It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems, and the need for automated techniques has been raised to enhance detection accuracy. This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack Ensemble Road Anomaly Detection (SERAD). SERAD is used to detect and classify the four most happening road anomalies, such as accidents, car fires, fighting, and snatching, through road surveillance videos with high accuracy. The SERAD adapted three pre-trained Convolutional Neural Networks (CNNs) models, namely VGG19, ResNet50 and InceptionV3. The stacking technique is employed to incorporate these three models, resulting in much-improved accuracy for classifying road abnormalities compared to individual models. Additionally, it presented a custom real-world Road Anomaly Dataset (RAD) comprising a comprehensive collection of road images and videos. The experimental results demonstrate the strength and reliability of the proposed SERAD model, achieving an impressive classification accuracy of 98.7%. The results indicate that the proposed SERAD model outperforms than the individual CNN base models.