Journal of Mobile Multimedia

Intelligent Frame Retention and Anomaly Detection with Notification Using YOLOv11m

2026-03-28T14:01:54+01:00

This study aims to solve the problem of video storage and improves the overall efficiency of cameras by adopting real-time anomaly detection, hence informing the user about any suspicious anomalies. The proposed trained model processes live video streams, identifying unusual events and anomalies such as theft, weapons, or violent activities. Simultaneously, a video storage optimization algorithm reduces redundant frames while maintaining movement detected video streams from CCTV surveillance. In addition, if the model detects an unusual event occurring in the live video stream, it immediately notifies the user about the type of anomaly and the location of the event that occurred. Experimental results demonstrate that the proposed system effectively detects anomalies with an average precision–recall score of 0.958 and an F1 confidence score of 0.93, ensuring reliable threat identification and detection. The model is robust and differentiates between normal and anomalous activities as justified by experimental results.

Developing Game-Based Character Education on Unity in Diversity for Elementary School Students

2026-03-03T21:56:06+01:00

Learning about Unity in Diversity can be challenging to implement effectively, as many students become disengaged with conventional lecture-based methods. This study aims to develop and evaluate a game-based learning model for character education that promotes the values of Unity in Diversity among elementary school students in Indonesia. In today’s increasingly diverse world, fostering understanding of different cultures and perspectives is essential. The research employs two public schools in Indonesia. Quantitative data were collected through structured questionnaires, while qualitative data were obtained from classroom observations and student feedback a mixed-methods design integrated with the Game Development Life Cycle (GDLC), encompassing six stages: initiation, pre-production, production, testing, beta, and release. The study involved a sample of elementary school students (N=55) from two public schools in Indonesia. Quantitative data were collected through structured questionnaires, while qualitative data were obtained from classroom observations and student feedback. The developed game integrates core values such as tolerance, respect, cooperation, and empathy within an interactive digital learning environment. Findings reveal that the game-based learning model enhances student engagement and supports the development of diversity-related values, with mobile-based platforms identified as the most feasible and effective medium for implementation. These results suggest that integrating game-based strategies provides a more engaging, accessible, and meaningful learning experience for young learners.

Efficient Multispectral Image Communication Using Compressed Sensing

2025-12-14T13:22:43+01:00

This paper explores a joint method for efficient compression as well as the reliable transmission of images. In image transmission, low dormancy and very fast data transmission are the main requirements of modern wireless communications systems. Efficient and reliable image transmission through wireless networks need to handle several challenges like adverse wireless channel conditions, the need for high power consumption, managing high computational complexity, and low error resilience capability of image compression schemes. This paper deals with the compressed sensing approach in combination with orthogonal frequency division multiplexing (OFDM) to take care of the above challenges. There are three important steps in compressive sensing: sparse signal representation, measurement collection, and sparse recovery. In this process, a measurement matrix is utilized to sample those elements which are significant for accurately depicting the signal in the measurement step. So, the design of precise measurement matrices is crucial for compressive sensing. This paper proposes a Lanczos measurement matrix which significantly improves the quality of the reconstructed image with minimum data. At the same time, the use of OFDM handles multipath fading channels for reliable transmission of the image data. The simulation results also compare the performance of several measurement matrices in terms of image quality through peak signal-to-noise ratio (PSNR) value, the structural similarity index, and the Bit Error Rate (BER) for transmission performance after passing through Additive White Gaussian Noise (AWGN) as well as multipath channels.

Unimodal Touch Behaviour-Based User Authentication Using Deep Learning and Swarm Intelligence for Smartphones

2025-05-06T12:02:54+02:00

Smartphones have become necessary in everyday life since they make communication, financial transactions, and data access easier. However, their broad use poses serious security risks, especially regarding ongoing user authentication. Traditional authentication techniques, including PINs, passwords, and patterns, only authenticate users at points of entry, leaving devices open to replay attacks, session hijacking, and spoofing. To overcome these constraints, the hybrid authentication approach proposed in this research uses multimodal touch behaviour for real-time identity verification. Using the Touchalytics dataset, this method combines motion sensor data from accelerometers, gyroscopes, and magnetometers with fine-grained touch attributes, including touch area, pressure, finger orientation, and typing dynamics. Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are combined in the system’s deep learning (DL) architecture for sequential touch analysis, and optimization approaches are used to improve model performance. The model captures detailed touch behaviour and motion sensor data, with hyperparameter tuning applied using Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CSO), and Sea-Horse Optimization (SHO). The CNN-LSTM + PSO model outperforms standalone DL models by achieving 99.86% accuracy with a False Acceptance Rate (FAR) of 0.0009, False Rejection Rate (FRR) of 0.0012, and Equal Error Rate (EER) of 0.001, according to extensive assessment on the Touchalytics dataset. For next-generation mobile security, this combination of Swarm Intelligence (SI) and DL provides a strong, flexible, and effective authentication architecture.