Surveillance cameras produce vast amounts of video data, posing a challenge for analysts due to the infrequent occurrence of unusual events. To address this, intelligent surveillance systems leverage AI and computer vision to automatically detect anomalies. This study proposes an innovative method combining 3D convolutions and long short‐term memory (LSTM) modules to capture spatiotemporal features in video data. Notably, a structured coarse‐level feature fusion mechanism enhances generalization and mitigates the issue of vanishing gradients. Unlike traditional convolutional neural networks, the approach employs depth‐wise feature stacking, reducing computational complexity and enhancing the architecture. Additionally, it integrates microautoencoder blocks for downsampling, eliminates the computational load of ConvLSTM2D layers, and employs frequent feature concatenation blocks during upsampling to preserve temporal information. Integrating a Conv‐LSTM module at the down‐ and upsampling stages enhances the model's ability to capture short‐ and long‐term temporal features, resulting in a 42‐layer network while maintaining robust performance. Experimental results demonstrate significant reductions in false alarms and improved accuracy compared to contemporary methods, with enhancements of 2.7%, 0.6%, and 3.4% on the UCSDPed1, UCSDPed2, and Avenue datasets, respectively.

In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video (ADSV). At the input of the ADSV, a shots boundary detection technique is used to segment prominent frames. Next, The Light-weight Convolution Neural Network (LWCNN) model receives the segmented frames to extract spatial and temporal information from the intermediate layer. Following that, spatial and temporal features are learned using Long Short-Term Memory (LSTM) cells and Attention Network from a series of frames for each anomalous activity in a sample. To detect motion and action, the LWCNN received chronologically sorted frames. Finally, the anomaly activity in the video is identified using the proposed trained ADSV model. Extensive experiments are conducted on complex and challenging benchmark datasets. In addition, the experimental results have been compared to state-of-the-art methodologies, and a significant improvement is attained, demonstrating the efficiency of our ADSV method.

Customer Relationship Management (CRM) systems, widely used in enterprises, have evolved into Software-as-a-Service (SaaS) platforms. With the advent of Customer Data Platforms (CDP), these systems continuously store customer behavior data for purposes such as creating single customer profiles, analyzing, tracking, and managing customer interactions from various perspectives. With the global expansion of the e-commerce market, research on customer analysis and classification optimized for the e-commerce environment has been actively conducted. The RFM (Recency, Frequency, Monetary) model is a straightforward method for classifying customers and is applied across various industries. However, in the e-commerce environment, where customers can access services at any time, there are limitations in collecting, storing, and reflecting customer behavior data for classification. To resolve these limitations, this paper proposes the RFMVDA (Recency, Frequency, Monetary, Visits, Durations, Actions) model. This model is designed to capture customer data, sessions, and behavior units suitable for the e-commerce environment. By utilizing theRFMVDAmodel for customer behavior-based segmentation and classification, we constructed a Deep Neural Network (DNN) to predict customer behavior-based classifications. As a result, the proposed model demonstrated a segmentation prediction accuracy of 92.98% for customers in the e-commerce environment.

Currently, the number of functions to improve user convenience in smartphone applications is increasing. In addition, more mobile applications are being loaded into mobile operating system memory for faster launches, thus increasing the memory requirements for smartphones. The memory used by applications in mobile operating systems is managed using software; allocated memory is freed up by either considering the usage state of the application or terminating the least recently used (LRU) application. As LRU-based memory management schemes do not consider the application launch frequency in a low memory situation, currently used mobile operating systems can lead to the termination of a frequently executed application, thereby increasing its relaunch time. This study proposes a memory management system that can efficiently utilize the main memory space by analyzing the application usage information. The proposed system reduces the application launch time by leaving the most frequently used or likely to be run applications in the main memory for as long as possible. The performance evaluation conducted utilizing actual smartphone usage records showed that the proposed memory management system increases the number of times the applications resume from the main memory compared with the conventional memory management system, and that the average application execution time is reduced by approximately 17%.