Evaluation of the effectiveness of rehabilitation therapy is often based on patient outcomes and functional improvement over time. However, traditional approaches are subjective and heavily influenced by expert perspectives and perceptions, which can lead to inconsistencies in research. This study developed a motion measurement system that improves body positioning by using computer vision techniques to quantitatively assess elbow flexibility and range of motion during rehabilitation. The system uses a novel posture estimation model that analyses video input to identify key body points and match body position, providing smooth, real-time feedback. In addition, the system is designed to be easy to use and simple, so it can be implemented in both clinical settings and home-based rehabilitation programs. Data validation is carried out using a reference dataset that has previously been analyzed, with an absolute mean error of approximately 5.00 degrees and a correlation coefficient of approximately 0.95. The results show that this system can be used to maintain one's own behavior. This technology supports the rehabilitation process by providing clear data, helping medical professionals and health care providers better assess patient progress and make more appropriate decisions. In addition, this technology can improve tele-rehabilitation services, especially for patients who cannot access medical facilities. With that in mind, limitations such as model issues and video quality variations can affect the accuracy of body-position determination.
Kok Swee Sim, Mahda Laina Arnumukti· International Journal on Adv...· 0 citations
Integrating multi-omics data to understand biological processes in human diseases is a complex bioinformatic task. Machine learning (ML), particularly deep learning (DL) models, offers a promising approach to multi-omics data integration and analysis. However, existing DL models generally integrate multi-omics data by concatenating the input data space or learned feature space, which is a sub-optimal approach. In addition, single classifiers are commonly used in DL-based methods, which can compromise the performance. Furthermore, the gradient descent optimization technique in DL suffers from a high computational cost and local sub-optimal solutions. To address these challenges, this article presents a novel cancer subtype classification framework using multi-omics integration and an ensemble-based parallel DL/ML architecture. Specifically, a multimodal autoencoder is used for effective feature learning across omics types, overcoming the limitations of naïve concatenation. A hybrid ensemble model comprising DL and ML learners with a meta-learner enhances classification robustness beyond single models. To improve optimization and computation, we incorporate a hybrid Back-Propagation and Particle Swarm Optimization (PSO) strategy and execute the entire framework on a parallel processing platform, reducing computation time while enhancing global search capability. The proposed framework is evaluated empirically with two benchmark data sets from The Cancer Genome Atlas (TCGA), namely the TCGA Pan-cancer and TCGA Breast Invasive Carcinoma (BRCA) data sets. The results indicate a high performance with accuracy rates of 89.51% and 90.9% for TCGA Pan-cancer and TCGA BRCA, respectively. The parallel implementation of the proposed framework reduces the computation time, resulting in a speed-up of 3 times and 2.5 times for TCGA Pan-cancer and TCGA BRCA, respectively. The findings ascertain the efficacy of the proposed framework for the classification of cancer subtypes, offering a promising solution for implementation in real-world environments.
Mohammed Nasser Al-Andoli, Shing Chiang Tan, Kok Swee Sim et al.· PeerJ Computer Science· 0 citations
The rapid advancement of deepfake generation technologies has fundamentally outpaced the forensic tools designed to detect and authenticate digital media. Traditional watermarking methods, while foundational, were not conceived for the adversarial complexity of multimodal content ecosystems where video, audio, and image signals are increasingly synthesized, blended, and redistributed at scale. This gap has made reliable media provenance one of the most pressing open problems in applied artificial intelligence. This mini review surveys the current landscape of transformer-based approaches to digital watermarking and deepfake detection, with a focus on their capacity to operate across multiple modalities within unified architectures. We trace the progression from classical signal-based watermarking to attention-driven deep learning frameworks, highlighting where transformer models offer meaningful resilience gains over legacy methods. We further examine emerging efforts to consolidate watermark embedding, forgery detection, and content authentication into integrated pipelines and discuss why such unification is both technically advantageous and practically necessary. The review closes by mapping the field's most consequential open challenges including cross-modal generalization, adversarial robustness, and benchmark scarcity and identifying the directions most likely to yield progress.
Kok Swee Sim, M. Islam· Frontiers in Artificial Inte...· 0 citations
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