Dynamical systems has a variety of applications for the new information generated during the time. Many phenomenons like physical, chemical or social are not static, then an analysis over the time is necessary. In this work, an experimental analysis of parameters of the model Echo State Network is performed and the influence of the kind of Complex Network is explored to understand the influence on the performance. The experiments are performed using the Rossler attractor.

Implementing predictive models in utility companies to detect Non-Technical Losses (i.e. fraud and other meter problems) is challenging: the data available is biased, and the algorithms usually used are black-boxes that can not be either easily trusted or understood by the stakeholders. In this work, we explain our approach to mitigate these problems in a real supervised system to detect non-technical losses for an international utility company from Spain. This approach exploits human knowledge (e.g. from the data scientists or the company's stakeholders), and the information provided by explanatory methods to implement smart feature engineering. This simple, efficient method that can be easily implemented in other industrial projects is tested in a real dataset and the results evidence that the derived prediction model is better in terms of accuracy, interpretability, robustness and flexibility.

Deep learning has produced big advances in artificial intelligence, but trained neural networks often reflect and amplify bias in their training data, and thus produce unfair predictions. We propose a novel measure of individual fairness, called prediction sensitivity, that approximates the extent to which a particular prediction is dependent on a protected attribute. We show how to compute prediction sensitivity using standard automatic differentiation capabilities present in modern deep learning frameworks, and present preliminary empirical results suggesting that prediction sensitivity may be effective for measuring bias in individual predictions.

The Artificial Intelligence for Edge Devices market study now available with Market Study Report, LLC, is a collation of valuable insights related to market size, market share, profitability margin, growth dynamics and regional proliferation of this business vertical. The study further includes a detailed analysis pertaining to key challenges, growth opportunities and application segments of the Artificial Intelligence for Edge Devices market. The Artificial Intelligence for Edge Devices market report delivers an exhaustive analysis of this industry vertical and comprises of insights pertaining to the market tendencies including profits estimations, periodic deliverables, current revenue, industry share and remuneration estimations over the forecast period. A summary of the performance evaluation of the Artificial Intelligence for Edge Devices market is offered in the report. It also includes crucial information concerning to the key industry trends and projected growth rate of the said market.

A fresh report titled "Artificial Intelligence In IoT Market" conveying key insights and providing a competitive advantage to clients through a comprehensive report. The report contains 123 pages which highly exhibit on up-to-date market analysis scenario, upcoming as well as future opportunities, revenue growth, pricing and profitability. An exclusive data and facts offered in this report is collected by research and industry experts' team. Research Trades proclaims the addition of new analytical data which helps to make informed business decisions. It has been abridged with a exhaustive description of the global Artificial Intelligence In IoT Market including overview, Types, Segments, Applications and Features of the market.

The report on the AI (Artificial Intelligence) Chip industry provides an in-depth assessment of the AI (Artificial Intelligence) Chip market including technological advancements, market drivers, challenges, current and emerging trends, opportunities, threats, risks, strategic developments, product advancements, and other key features. The report covers market size estimation, share, growth rate, global position, and regional analysis of the market. The report also covers forecast estimations for investments in the AI (Artificial Intelligence) Chip industry from 2020 to 2027. The report is furnished with the latest market dynamics and economic scenario in regards to the COVID-19 pandemic. The pandemic has brought about drastic changes in the economy of the world and has affected several key segments and growth opportunities.

In machine learning, one must acquire labels to help supervise a model that will be able to generalize to unseen data. However, the labeling process can be tedious, long, costly, and error-prone. It is often the case that most of our data is unlabeled. Semi-supervised learning (SSL) alleviates that by making strong assumptions about the relation between the labels and the input data distribution. This paradigm has been successful in practice, but most SSL algorithms end up fully trusting the few available labels. In real life, both humans and automated systems are prone to mistakes; it is essential that our algorithms are able to work with labels that are both few and also unreliable. Our work aims to perform an extensive empirical evaluation of existing graph-based semi-supervised algorithms, like Gaussian Fields and Harmonic Functions, Local and Global Consistency, Laplacian Eigenmaps, Graph Transduction Through Alternating Minimization. To do that, we compare the accuracy of classifiers while varying the amount of labeled data and label noise for many different samples. Our results show that, if the dataset is consistent with SSL assumptions, we are able to detect the noisiest instances, although this gets harder when the number of available labels decreases. Also, the Laplacian Eigenmaps algorithm performed better than label propagation when the data came from high-dimensional clusters.

Semantic image segmentation is one of fastest growing areas in computer vision with a variety of applications. In many areas, such as robotics and autonomous vehicles, semantic image segmentation is crucial, since it provides the necessary context for actions to be taken based on a scene understanding at the pixel level. Moreover, the success of medical diagnosis and treatment relies on the extremely accurate understanding of the data under consideration and semantic image segmentation is one of the important tools in many cases. Recent developments in deep learning have provided a host of tools to tackle this problem efficiently and with increased accuracy. This work provides a comprehensive analysis of state-of-the-art deep learning architectures in image segmentation and, more importantly, an extensive list of techniques to achieve fast inference and computational efficiency. The origins of these techniques as well as their strengths and trade-offs are discussed with an in-depth analysis of their impact in the area. The best-performing architectures are summarized with a list of methods used to achieve these state-of-the-art results.

The Global Artificial Intelligence and Robotics in Aerospace and Defense Market report studies the market comprehensively and provides an all-encompassing analysis of the key growth factors, Artificial Intelligence and Robotics in Aerospace and Defense market share, and the newest developments. Also, the Artificial Intelligence and Robotics in Aerospace and Defense Industry Market report provides growth rate, market demand and supply, and market potential for each geographical region. The Artificial Intelligence and Robotics in Aerospace and Defense report gives information about the Artificial Intelligence and Robotics in Aerospace and Defense market trend and share, market size analysis by region, and analysis of the global market size. The market study analysis presents an analysis of market share and segments by region and growth rate. Regional breakdown includes an in detail study of the key geological regions to gain a better accepting of the market and provide an accurate analysis.

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