Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yield can help growers improve the environmental control for better production, match supply and market demand and lower costs. Recent developments in Machine Learning (ML) and, in particular, Deep Learning (DL) can provide powerful new analytical tools. The proposed study utilises ML and DL techniques to predict yield and plant growth variation across two different scenarios, tomato yield forecasting and Ficus benjamina stem growth, in controlled greenhouse environments. We deploy a new deep recurrent neural network (RNN), using the Long Short-Term Memory (LSTM) neuron model, in the prediction formulations. Both the former yield, growth and stem diameter values, as well as the microclimate conditions, are used by the RNN architecture to model the targeted growth parameters. A comparative study is presented, using ML methods, such as support vector regression and random forest regression, utilising the mean square error criterion, in order to evaluate the performance achieved by the different methods. Very promising results, based on data that have been obtained from two greenhouses, in Belgium and the UK, in the framework of the EU Interreg SMARTGREEN project (2017-2021), are presented.

Knowledge graph embeddings rank among the most successful methods for link prediction in knowledge graphs, i.e., the task of completing an incomplete collection of relational facts. A downside of these models is their strong sensitivity to model hyperparameters, in particular regularizers, which have to be extensively tuned to reach good performance [Kadlec et al., 2017]. We propose an efficient method for large scale hyperparameter tuning by interpreting these models in a probabilistic framework. After a model augmentation that introduces per-entity hyperparameters, we use a variational expectation-maximization approach to tune thousands of such hyperparameters with minimal additional cost. Our approach is agnostic to details of the model and results in a new state of the art in link prediction on standard benchmark data.

Recommender Systems are becoming more and more relevant as the amount of information on "The Internets" is exponentially increasing: Finding what you might enjoy from books, movies, games to who to follow on Instagram becomes increasingly difficult. Moreover, we (the users) require faster interactions with the products we use on a daily basis, so we don't feel like we waste time (even though we do it more than ever before in human history). As the amounts of data increase, your computational resources might have a hard time to produce fast enough results. Here, we'll have a look at a succinct implementation of a Recommender System that is both the basis of many real-world implementations and is easy to understand. Traditionally, recommender systems are built around user ratings given for a set of items, e.g.

A logistic regression model is said to be statistically significant only when the p-Values are less than the pre-determined statistical significance level, which is ideally 0.05. The p-value for each coefficient is represented as a probability Pr( z). We see here that both the coefficients have a very low p-value which means that both the coefficients are essential in computing the response variable. The stars corresponding to the p-values indicate the significance of that respective variable. Since in our model, both the p values have a 3 star, this indicates that both the variables are extremely significant in predicting the response variable.

We're going to let XGBoost, LightGBM and Catboost battle it out in 3 rounds: In each round here are the steps we'll follow: You can find the accompanying code here. Let's start with the top findings. LightGBM is the clear winner in terms of both training and prediction times, with CatBoost trailing behind very slightly. XGBoost took substantially more time to train but had reasonable prediction times. We also want to know why the model is making its predictions.

Mean Shift is a hierarchical clustering algorithm. In contrast to supervised machine learning algorithms, clustering attempts to group data without having first been train on labeled data. Clustering is used in a wide variety of applications such as search engines, academic rankings and medicine. As opposed to K-Means, when using Mean Shift, you don't need to know the number of categories (clusters) beforehand. The downside to Mean Shift is that it is computationally expensive -- O(n²).

We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses transportation of measures, convex optimization, and ideas from probabilistic graphical models to yield robust ensemble approximations of the filtering distribution in high dimensions. Our approach can be understood as the natural generalization of the ensemble Kalman filter (EnKF) to nonlinear updates, using stochastic or deterministic couplings. The use of nonlinear updates can reduce the intrinsic bias of the EnKF at a marginal increase in computational cost. We avoid any form of importance sampling and introduce non-Gaussian localization approaches for dimension scalability. Our framework achieves state-of-the-art tracking performance on challenging configurations of the Lorenz-96 model in the chaotic regime.

A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on density-based clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.

The fifth generation (5G) and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some challenges in complex dynamic wireless environments, including significant security overhead, low reliability, as well as difficulty in pre-designing authentication model, providing continuous protections, and learning time-varying attributes. In this article, we envision new authentication approaches based on machine learning techniques by opportunistically leveraging physical layer attributes, and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/unsupervised/reinforcement learning algorithms. In a nutshell, the machine learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous and situation-aware device validation under unknown network conditions and unpredictable dynamics.

In this study, we investigated multi-modal approaches using images, descriptions, and title to categorize e-commerce products on Amazon.com. Specifically, we examined late fusion models, where the modalities are fused at the decision level. Products were each assigned multiple labels, and the hierarchy in the labels were flattened and filtered. For our individual baseline models, we modified a CNN architecture to classify the description and title, and then modified Keras' ResNet-50 to classify the images, achieving F1 scores of 77.0%, 82.7%, and 61.0%, respectively. In comparison, our tri-modal late fusion model can classify products more accurately than single modal models can, improving the F1 score to 88.2%. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the accuracy of multi-label classification problems.