Our AI research team explores cutting-edge innovations, advancing automation, machine learning, and intelligent systems to revolutionize industries. We develop transformative AI solutions that enhance efficiency, decision-making, and user experiences, shaping a smarter, more connected, and technology-driven future for businesses worldwide.
Revolutionising Innovation: Cutting-Edge AI Research for a Smarter Future
Remote Reading of Surgical Monitor's Physiological Readings: An Image Processing Approach
The global impact of infectious diseases like COVID-19 has highlighted the need for remote patient monitoring. Surgical ICU monitors track critical patients 24/7, but most lack output ports for data transfer to auxiliary devices, limiting post-processing and advanced analysis for improved patient care.
Comparative Analysis of Matrix Factorization Techniques for Collaborative Filtering for Recommendation Systems
Enhancing user experience in e-commerce relies on recommendation systems. This study implements and compares matrix factorization models using a dataset from an online marketing platform stored in MongoDB. SVD, NMF, ALS, and Neural Network Matrix Factorization models were tested for effectiveness.
An In-Depth Exploration of Annotation: Methods, Applications, and Implications
This paper explores annotation’s purpose, methods, and applications across various domains. Using a systematic review, it examines text, image, and data annotation, covering manual, semi-automated, and automated approaches. It discusses challenges, advancements in ML-based tools, and annotation’s impact on research, ethics, and AI development.
Comprehensive Analysis of Segmentation models for Multiclass Segmentation
This paper explores image segmentation for automating planogram compliance in retail. It aims to identify the best model for accurately segmenting inner and outer shelves in supermarket racks. Seven architectures—U-Net, LinkNet, FPN, PAN, PSPNet, MANet, and DeepLabV3—were trained and evaluated for optimal performance.
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