Securly’s Post

Student surveys often fall short of our expectations in terms of accuracy. - Are we receiving genuine responses, or are students merely rushing to complete the survey? - When the data is finally compiled, how much has actually changed for the school or for individual students? - Were some students absent on the survey day, or do we have new students who never took the survey? -Who on the staff has the time and resources to analyze the data? These challenges are familiar to anyone who has spent time in the classroom. The school year ends, and despite your best efforts, there are always a few students who remain a constant topic of discussion in team meetings. With Discern, you can analyze existing data across various frameworks to obtain real-time, impactful insights about individual students and the entire district. THEN... use all that extra time spent trying to decipher data to create a plan that can be monitored over time. PLUS... you can even receive automatic reports for you and your team, allowing you to see the impact firsthand and make adjustments as needed.

“Machine Learning Reveals User Data Speed Factors” 🚀 Based on our work with the customer, we can now analyze the positive and negative effects on User Data Speed using the Machine Learning Algorithm. For example, as shown below, we can pinpoint the negative impact of Active Users within specific Mbps ranges. Additionally, we assess whether factors like interference, high utilization, active users, or connected users affect the Average User Speed at each site. Traditional KPI analyses fall short compared to this approach. #4G #dataanalysis #kpi #ML ref: Erkan Keskin

"What is the minimum number of experiments, or calculations, required to find an optimal solution?" ask Wu, Walsh and Ganose in today's featured #DigitalDiscovery Perspective. Discover how to use Bayesian optimisation to locate minima/maxima in automated and machine learning workflows this #openaccess article: https://lnkd.in/eZ5bAvUJ

🚀 **Start the School Year Right with Nexus by Loop!** 📊 Begin the school year on the right foot by tracking student performance from day one. With Nexus by Loop, you can effortlessly monitor crucial data like attendance, grades, and behavior in real time. This enables early identification of trends that significantly impact student success. Empower your schools with AI-powered insights that facilitate making timely, evidence-based decisions. Take advantage of cutting-edge technology to drive positive outcomes for your students. Curious to learn more? 💡 Schedule a demo today and witness firsthand how Nexus can revolutionize your district's data approach. #EdTech #AI #StudentPerformance #DataDrivenDecisions Explore Nexus: https://www.asknexus.ai/ Book a Nexus Demo: https://lnkd.in/gnaw-JcE

Stop Guessing, Start Predicting with Tree-Based Models Discover how powerful machine learning techniques like CART, Random Forest, and TreeNet can help you predict machine failures and optimize costs. This is your opportunity to learn how data can drive better decisions, reduce downtime, and save money! 👨💻 Don’t miss it—register now! https://lnkd.in/g5WSpxWj #PredictiveAnalytics #MachineLearning #DataScience #OperationalEfficiency

🌟 Exploring Dimensionality Reduction in Machine Learning 🌟 In the world of machine learning, dimensionality reduction plays a crucial role in simplifying data for easier interpretation and visualization. While modern algorithms and powerful hardware can handle high-dimensional data, reducing dimensionality can still offer significant benefits. ---- #MachineLearning #DimensionalityReduction #DataScience #PCA #UMAP #Autoencoders #DataVisualization #TechInsights

Machine learning can make the identification of equipment tags in industrial diagrams a seamless process by automatically detecting and extracting relevant symbols and data with high accuracy. #DataHack #AIExtractsP&IDTags

📢#Recommendedpaper "#Machine #Learning for #Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life Data" by Minje Seok et al. 📌Find the full paper here: https://lnkd.in/gffaDE2s

Our next featured #DigitalDiscovery article comes from Wang, Hippalgaonkar, et al. and shows how machine learning approaches using Materials Project data and BoltzTrap calculated results can be used to predict new high-performance thermoelectrics in an interpretable way. Read more: https://lnkd.in/eimaTn7k

From The Archive: Crafted uniquely for each consumer, variations are tailored based on individual tastes and preferences, utilising machine learning for personalised decisions. #DynamicContent #MachineLearning