Exciting new blog post alert! Check out this comprehensive comparison of harmful algal bloom forecasting methods, highlighting the value of stream and batch learning. The study developed a machine learning workflow utilizing seven algorithms to predict toxic dinoflagellate population. Find out which model emerged as the most effective predictor. Read the full article here: https://bit.ly/3I6P4fu.

Our latest research paper presents a hybrid approach for tackling the problem of irrelevant features in feature selection process. Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for the problem being studied. These irrelevant features reduce both the efficiency and accuracy of the algorithm. Our latest research paper presents a hybrid approach for tackling the problem of feature selection. "An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms", Data 2024, MDPI. 9(2), 20; https://lnkd.in/dET8eHAw

Our paper is out! We propose different classes of methods to establish the relatedness of products that overcome co-occurrences-based methods in predicting the development paths of countries

How has weather prediction evolved since 1950? How is machine learning reshaping this field? Find out in this #AIforGood webinar as we present WeatherBench2 from @google 📆 29 November @ 17:00 CET / 11:00 EST https://loom.ly/hCibtwU

Over the past 5 years, I've witnessed the growing importance of data in the built environment. At first, I was simply looking to overcome data scarcity, challenge recognized across the industry in various machine learning applications. This led to the exploration of solutions like simulation environments, synthetic data generation, and advanced techniques such as transfer learning and zero-shot learning. Then, I shifted my attention to the idea of merging detailed machine learning models with a solid understanding of the underlying physics. In my opinion, the key driver here is a detailed ontology, that provides a well-defined framework and understanding of both physical and digital world. While the goal is clear, the path to achieving it is still under discussion and it's interesting to see approaches that tend to skew toward either side. I discuss this concept in the latest article of my newsletter, Bricks To Bytes https://lnkd.in/eZCvYGnZ Let me know what do you think!

Exciting news! Check out this thought-provoking blog post discussing the future of random sets and belief functions. The post delves into the potential development of statistical reasoning, the geometric approach to uncertainty, and its application in high-impact areas like climate change, machine learning, and statistical learning theory. Dive into the details here: https://bit.ly/3Sok54o [math.ST]

Do you know how decision trees and random forests drive the world of machine learning? This comprehensive guide dives deep into the mechanics and applications of these powerful algorithms. Inside, you'll discover: ✅ The fundamentals of decision trees and how they make decisions ✅ Intricate workings of random forests and their superiority in handling complex data sets ✅ Real-world applications that are reshaping industries ✅ Practical examples and case studies to bring the theory to life What do you think is the most exciting application of decision trees and random forests in your field? Thanks to Nikola Pulev

Check out this amazing Blog based on the Major differences between the KNN and KMeans Algorithms Machine Learning by @amrit agarwal

Hi guys , This certificate is for the completion of minor project ,In this project i learn about the prediction concept in machine learning. And i learn about how to train and test the data . The project is about the titanic survival prediction using machine learning .

📢 Exciting Update! 🌟 Discover the latest trends in Federated Learning: From Model Fusion to Federated X Learning. This in-depth survey explores the potential of federated learning to integrate with other learning algorithms. It examines enhanced federated averaging algorithms and highlights the use of model fusion techniques such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Get a comprehensive overview of the state of the art, challenges, and future directions in this evolving field. 🔍 Read the full blog post here: [Link](https://bit.ly/46uFPiR) #FederatedLearning #MachineLearning #ArtificialIntelligence