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The water crisis, global warming and climate changes have become recently prominent world issues. Saving and conserving water have become, therefore, an imperative for water resources’ sustainability. In this context, dramatic innovations have come to light, e.g., Precision Agriculture. Numerous research projects on this subject have been conducted, as illustrated not only by the huge number of research works in this topic, but also by the dozens of surveys dedicated to. Surveys regarding smart agriculture are of two categories, surveys on smart agriculture in general in which smart irrigation is a subtopic among others, and surveys dedicated to smart irrigation. Existing surveys, however, suffer from limitations including low paper numbers, linear description without structural classification, and missing key information. To fill this gap, we propose, in this paper, a survey on SI that classifies the large body of literature into categories in a structured way. To this end, we have employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to determine the inclusion or exclusion of articles. We have reviewed a total of 610 publications out of which 227 met the inclusion criteria. We have categorized the selected references into three conceptual clusters corresponding to: (1) 41.8% focusing on field measurements, based on IoT sensors, (2) 37% involving Remote Sensing (RS) and (3) 21.1% Artificial Intelligence (AI) methods. Each category is thoroughly described, including the inputs and outputs, along with a description of the various tasks employed in irrigation management. This systematic re-view is expected to serve as a useful reference for research on smart irrigation management methods.

Building upon the recent advances and successes in the application of deep learning to the medical field, we propose in this work a new approach to detect and classify early-stage Alzheimer patients using online handwriting (HW) loop patterns. To cope with the lack of training data prevalent in the tasks of classification of neuro-degenerative diseases from behavioral data, we investigate several data augmentation techniques. In this respect, compared to the traditional data augmentation techniques proposed for HW-based Parkinson detection, we investigate a variant of Generative Adversarial Networks (GANs), DoppelGANger, especially tailored for times series and hence suitable for synthesizing realistic online handwriting sequences. Based on a 1D-Convolutional Neural Network (1D-CNN) to perform Alzheimer classification, we show, on a real dataset related to HW and Alzheimer, that our DoppelGANger-based augmentation model allow the CNN to significantly outperform both the current state of the art and the other data augmentation techniques.

Deep neural networks have been widely used in several complex tasks such as robotics, self-driving cars, medicine, etc. However, they have recently shown to be vulnerable in uncertain environments where inputs are noisy. As a consequence, the robustness of neural networks has become an essential property for their application in critical systems. Robustness is the capacity to take the same decision even when inputs are disturbed under different types of perturbations, including adversarial attacks. The great difficulty today is providing a formal guarantee of robustness, which is the context of this paper. To do so, abstract interpretation, a popular state-of-the-art method, consisting of converting the layers of the neural network into abstract layers, has been recently proposed. An abstract layer can act on a geometric abstract object or shape comprising implicitly an infinite number of inputs rather than an individual input. In this paper, we propose a new mathematical formulation of an abstract transformer to convert a LeakyReLU activation layer to an abstract layer. Moreover, we implement and integrate our transformer into the ERAN tool. For validation, we assess the performance of our transformer according to the LeakyReLU hyperparameter, and we study the robustness of the neural network according to the input perturbation intensity. Our approach is evaluated on three different datasets: MNIST, Fashion and a robotic dataset. The obtained results demonstrate the efficacy of our abstract transformer in terms of mathematical formulation and implementation.

Artificial Neural Networks (ANNs) have amassed unprecedented success in information processing ranging from image recognition to time series prediction. The success can largely be attributed to the availability of large datasets for training and the increased complexity of the models. Unfortunately, for some applications only a limited amount of samples is available for training. Fewer training samples increases the risk of over-fitting and poor generalization especially in high complexity models. Moreover, complex models with a large number of trainable parameters require more energy to train and optimize compared to simpler ones. In this paper, to the best of our knowledge, we propose the first use of ANNs for Early Stage Alzheimer Disease classification (ES-AD) from the handwriting (HW). We propose using a framework for building Recurrent Neural Networks (RNNs) known as Reservoir Computing (RC), both numerically and experimentally, that simplifies training by optimizing the output layer only. We also propose the Bidirectional Long Term Short Term (BiLSTM) and Convolutional Neural Network (CNN) methods for comparison. For a fairer comparison, we not only consider the accuracies but also the energy costs incurred to obtain the respective accuracies in order to assess the accuracy-efficiency trade-off. Our numerical and experimental results show that RC yields a classification accuracy of 85%, which is 3% worse than that of BiLSTM and 2% better than that of CNN, at a relatively lower training and significantly lower inference costs. We hope that our findings highlight the importance of examining the accuracy-efficiency trade-off of various models in the community in order to reduce the overall impact of ANNs training on the environment.