In this study, we present a non-invasive glucose prediction system that integrates Near-Infrared (NIR) spectroscopy and millimeter-wave (mm-wave) sensing. We employ a Mixed Linear Model (MixedLM) to analyze the association between mm-wave frequency S_21 parameters and blood glucose levels within a heterogeneous dataset. The MixedLM method considers inter-subject variability and integrates multiple predictors, offering a more comprehensive analysis than traditional correlation analysis. Additionally, we incorporate a Domain Generalization (DG) model, Meta-forests, to effectively handle domain variance in the dataset, enhancing the model's adaptability to individual differences. Our results demonstrate promising accuracy in glucose prediction for unseen subjects, with a mean absolute error (MAE) of 17.47 mg/dL, a root mean square error (RMSE) of 31.83 mg/dL, and a mean absolute percentage error (MAPE) of 10.88%, highlighting its potential for clinical application. This study marks a significant step towards developing accurate, personalized, and non-invasive glucose monitoring systems, contributing to improved diabetes management.

Stochastic processes defined on integer valued state spaces are popular within the physical A combination of factors such as non-linear dynamics, and biological sciences. These models are partial observation and a complicated latent structure necessary for capturing the dynamics of small makes the inference of parameter posteriors a systems where the individual nature of the challenging problem. The likelihood is generally intractable populations cannot be ignored and stochastic and although methods that can sample from effects are important. The inference of the the exact posteriors exist--many based on simulation parameters of such models, from time series of the model itself--these can become computationally data, is difficult due to intractability of the prohibitive in many situations (Doucet et al., 2015; likelihood; current methods, based on simulations Sherlock et al., 2015). Observations of a system with of the underlying model, can be so computationally low noise are particularly challenging for simulation expensive as to be prohibitive.

With the rise of interest and the number of machine learning projects (self-driving car, facial recognition, recommendation systems), traditional software development has shifted from hard-coded rules to data-estimated rules a.k.a. A set of new challenges arose for building reliable and stable information systems that rely on imperfect data-driven models, such as model versioning, deployment, monitoring, explainability and reproducibility. There is a whole new set of software engineering best practices that comes with the use of data-driven models in order to tackle those challenges, called MLOps. In order to get a broader view of what MLOps is, I recommend you to take a look at Jamila's article: Why MLOps is so important to understand?. The main purpose of MLOps is to make your entire ML project lifecycle automated and reproducible.

Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled via tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment.

By Johana Moreno, Product Owner The insurance industry has ushered in a new digital era in which customer behavior and preferred interaction is being converted to digital, customers want their problems solved quickly, and new generations prefer services with a rich digital experience. Insurers are looking for more automation and intelligent technologies to address all these trends, which have been exacerbated by the Covid crisis. This is where AI fills the gap and occupies a large space for automation and services with exceptional customer experience.  As an AI company, Zelros has been successfully using its machine learning models for various customers, and a wide range of applications in the insurance industry overcoming many challenges. On the one hand, implementing accountable tools to demonstrate the positive impact of our AI models for very demanding customers, and on the other, providing the gold service and reducing time to market. These have allowed Zelros to reinvent and optimize its process without compromising quality.  Machine learning is not an insignificant task, behind the scenes all our teams need an important organization to bring high-quality models to production. It requires the combination of two skills Data Science and Devops. AI software editors are facing difficulties to deliver and enhance AI solutions. Actions like data cleaning, data collecting, model training and validation, model deployment, and retraining are most of the time performed manually. This can mislead to operational errors and impacts on productivity and business performance. At Zelros, we believe that culture and environment based on ML technology can bring high business value. Ensuring clear governance for AI lifecycle processes and good automation technology contribute to a robust, transparent, and trustworthy AI.  To respond to these challenges, the Zelros platform provides a sustainable cycle for delivering ML into production, a way to orchestrate Data scientists and system integrators activities to work better together, to gain customer confidence and to found our AI solution on transparency and fairness AI principles. Benefits of using our MLOps platform : Benefit 1: Reduce time on data collection and data preparation  Data Scientists, systems integrators and solution engineers used to spend a lot of time with repetitive data acquisition or data preprocessing tasks before they could get their hands on the model and use our use cases. However, these tasks were fastidious and costly as many highly skilled resources were allocated before the model was built. MLOps can widely benefit data scientists and software engineers to reduce these operational tasks.  We wanted to reduce time connecting customers’ data to Zelros AI platform and to be able to leverage all use cases with fast data connectors. Obtaining up-to-date data is the most important thing to provide a powerful algorithm. For this reason, we paid special attention to data normalization, building and creating a standardized data model that would speed up the deployment process. Normalize data: A standard model is a data architecture where the data is stored, and customers can provide and add information that fits the AI use case. This normalized data provides the capability to use a centralized data environment with all features in one place rather than merging files and overheads from all different data sources every time a new feature is implemented and repeating it for each client. Data scientists can now work on one centralized data environment that respects data protection and data handling policies. Ensure Accuracy: Zelros guides its system in a process cycle in which data is regularly updated, allowing AI to evolve with up-to-date data to ensure the AI model’s response to the behavior and representation of the last population. Data scientists don’t worry anymore about updating data and focus only on model performance.   Benefit 2: Automate Model Building (Ready to use)  After data scientists and system integrators collected and cleaned up data, they had to manually create, validate and deploy the model. These actions could mislead to errors and lead to overrun in operational costs.  To truly create efficiency in operational tasks, training and deployment pipelines need to be automated. Automation can benefit Data scientists to focus on what they do best, extracting business-focused insights, research and looking for innovation and revolutionary techniques to solve AI Ethics issues. The lack of automation was one of the main difficulties; we transformed our traditional pipelines into an AutoML pipeline where our data scientist can simply select the use case and generate a specialized insurance model in a click. This fully automated pipeline continuously trains models resulting in a ready-to-use API.  Most of our customers had a long lifecycle for updating their software with the difficulty of upgrading their legacy systems. Besides, every client use case is unique, and the way models’ predictions are used can differ from customer to customer (we do not use data from one client to another). To facilitate interconnections between clients and our platform, Zelros supplies an API collection included on Zelros MLOps automatization pipeline, allowing us to cut the deployment time from 4 to 2 months.  Benefit 3: Accelerate the validation process The biggest AI lifecycle challenge is to scale from a small project to a large production system. To move forward, validation tools and transparency are key in the decision-making process, which sometimes requires validation from the business to the legal department. Stakeholders must be able to rely on measurable information before taking the big step.  Responsible AI is one of the greatest concerns at Zelros and we pay big attention to this principle. AI automation approach also applies to documentation, and Zelros MLops pipeline includes an Ethical and Fairness report, detailing the AI model in terms of processing, input data, prediction, completeness, behaviors,  and other statistical metrics.  With a plurality of stakeholders on AI projects, automatic reporting has demonstrated its advantages, such as communication and validation facilitator. The insurance and finance industries are very regulated sectors where decisions made by AI algorithms need to be transparent and follow a strict process. Reporting can facilitate the work between Insurers and external regulators like ACPR or BaFin. For example, […]