benchmarking-yolov8-v13-architectures-for-intelligent-real-time

Benchmarking YOLOv8-v13 Architectures for Intelligent Real-Time Cattle Monitoring and Data-Driven Farm Management in Precision Livestock Farming

Real-time and accurate cattle behavior detection is crucial for improving animal welfare and optimizing livestock management. To address this, this study ...

seamless-multimodal-human-robot-communication-integration-techniques

Seamless Multimodal Human-Robot Communication: Integration Techniques in Human&#8211;Computer Interaction

Seamless multimodal human-robot communication has become essential as social, assistive, and educational robots move into everyday settings like homes, ...

a-dynamic-written-corrective-feedback-framework-integrating-ai-agent

A Dynamic Written Corrective Feedback Framework Integrating AI Agent Delivery for Structured and Iterative Essay Support

Automated writing feedback systems are prevalent, yet most deliver static, fragmented comments that provide limited scaffolding for revision. This study ...

Universiti Sains Malaysia

Medication non-adherence remains a significant global healthcare challenge, resulting in inadequate disease management, increased hospitalisations, and higher healthcare costs. Mobile health (mHealth) applications have emerged as promising digital health tools for enhancing medication adherence through real-time monitoring, personalised reminders, artificial intelligence (AI)-driven interventions, and improved patient engagement.

Exploring mHealth interventions for medication management: a scoping review of digital tools, implementation barriers, and patient outcomes.

Real-time and accurate hand gesture detection is essential for safe and intuitive Human-Robot Interaction (HRI), enabling robots to interpret non-verbal cues and respond appropriately in dynamic environments. This research evaluates the effectiveness of YOLOv8n through YOLOv13n models in recognizing static hand gestures from the TSL detection dataset, which includes 5469 grayscale images across 31 gesture classes. The models underwent training with uniform data augmentation protocols and were assessed using object detection metrics including precision, recall, and mean average precision computed at an IoU threshold of 0.50 as well as over the interval from 0.50 to 0.95. The evaluation of computational efficiency involved metrics such as how fast the model infers, its frame rate, size and the total training duration. YOLOv9t exhibited the most robust detection accuracy across all evaluated metrics, achieving the highest mean mAP at 0.50 (0.990), mAP at 0.50 to 0.95 (0.876), precision (0.975), and recall (0.966). In contrast, YOLOv10n achieved the lowest inference latency (0.7 ms). These findings highlight the trade-off between accuracy and efficiency in gesture detection and show that YOLOv9t and YOLOv10n represent strong choices for accuracy and latency-critical applications, respectively.

Benchmarking YOLOv8 to YOLOv13 for robust hand gesture recognition in human-robot interaction.

Large language models (LLMs) are integral to cloud-based AI applications, offering robust capabilities for multimodal data processing and retrieval. However, deploying LLMs in environments with multiple users presents notable difficulties, especially in protecting sensitive information. In this study, we present PMIRS, a system designed to support secure, privacy-focused retrieval of multimodal image and text data. PMIRS allows users to transmit images or text to a simulated cloud-based LLM environment safely in a privacy-preserving manner. PMIRS mitigates privacy risks through a combination of obfuscation techniques, encrypted inference and federated learning. Specifically, federated learning is used to fine-tune a lightweight model adapted from the official CLIP codebase by OpenAI. During inference, query embeddings are first obfuscated through block-wise projection and then encrypted using AES in CBC mode with 128-bit keys to protect query content from unauthorized access. Additionally, the Diffie-Hellman algorithm is employed for secure key management in multi-user environments. Experimental results on three semantic domains from the customized Phrase-ImageNet variant of the ImageNet dataset, constructed by rewriting ImageNet labels into natural-language phrases, demonstrate that PMIRS achieves F1-scores up to 0.92, with precision exceeding 0.90 in small-to-medium repositories, and retrieval latency consistently under 180 milliseconds. Compared to the CLIP baseline, PMIRS improves average F1-score by 7.67% while maintaining comparable precision. These results underscore PMIRS's practical value for secure, efficient, and privacy-preserving multimodal retrieval. Beyond our controlled experiments, PMIRS has the potential to support real-world applications such as medical image retrieval, privacy-conscious customer service bots, and enterprise data management under regulatory frameworks like GDPR and HIPAA.

ArticleTotal : 3	Year
Publication title	2025
Publication title	2026
Publication title	2026

Article	Year
Exploring mHealth interventions for medication management: a scoping review of digital tools, implementation barriers, and patient outcomes. PeerJ. Computer science\| PubMed ID: 40989380	2025
Benchmarking YOLOv8 to YOLOv13 for robust hand gesture recognition in human-robot interaction. Scientific reports\| PubMed ID: 41238788	2025
A privacy-preserving multi-user retrieval system for multimodal artificial intelligence. Scientific reports\| PubMed ID: 41735504	2026

Yifang Gao

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