Components of Robots were not used in literature until Karel Capek's play "Rossum's Universal Robots" in 1921. The first motion picture to include a robot that looked like a human was "Metropolis" in 1926. Robots are now a common sight in our daily lives. Components of Robots now work in our warehouses and manufacturing facilities; explore far-off planets; assist us in inspecting our infrastructure sites, and even help us build entirely new ones. But how do robots truly function?

Ball bearing joints are a critical component in all rotating machinery, and detecting and locating faults in these joints is a significant problem in industry and research. Intelligent fault detection (IFD) is the process of applying machine learning and other statistical methods to monitor the health states of machines. This paper explores the construction of vibration images, a preprocessing technique that has been previously used to train convolutional neural networks for ball bearing joint IFD. The main results demonstrate the robustness of this technique by applying it to a larger dataset than previously used and exploring the hyperparameters used in constructing the vibration images.

Your learning system is often called an agent, in most modern literature. This term includes more than just the neural network estimator in recent algorithms. The agent interacts with an environment to solve a task. We'll go into further details later into what constitutes the environment, as it can become problematic. The environment relies on two main components: its transition and reward functions.

Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice. DCE and similar datasets of dynamic medical data tend to contain redundant information on the spatial and temporal components that may not be relevant for detection of the object of interest and result in unnecessarily complex computer models with long training times that may also under-perform at test time due to the abundance of noisy heterogeneous data. This work attempts to increase the training efficacy and performance of deep networks by determining redundant information in the spatial and spectral components and show that the performance of segmentation accuracy can be maintained and potentially improved. Reported experiments include the evaluation of training/testing efficacy on a heterogeneous dataset composed of abdominal images of pediatric DCE patients, showing that drastic data reduction (higher than 80%) can preserve the dynamic information and performance of the segmentation model, while effectively suppressing noise and unwanted portion of the images.

The Industrial revolution 4.0 marks the birth of digitization and automation of the manufacturing processes called – 'Smart Manufacturing.' Industry 4.0 however, is not a new technology and nor it is a business discipline, but a new approach called'Smart Manufacturing' which is fully integrated, collaborating manufacturing systems that respond in real-time to meet the changing conditions and demands in the factory, in the supply network and customer needs. This new approach will help the manufacturers to achieve the results that weren't possible a decade ago. Smart Factory which is at the heart of the Industry 4.0 will take on board the Information and communication technology (ICT) for the evolution in the production line and supply chain that integrates a much higher level of digitization and automation. It simply refers to machines using self-configuration, self-optimisation, and artificial intelligence to perform complex tasks for delivering vastly superior, high quality, and cost-efficient goods and services. The Smart Factory can also be defined as a factory where the Cyber-Physical Systems (CPS) communicates over the IoT and assist machines and people in the execution of their tasks.

The world of marketing is no stranger to buzzwords. While many marketing buzzwords words start off as real strategies or tactics that can improve a company's marketing efforts, their meanings often become diluted the more they are used. Over the years, we've seen words like "sales enablement," "social media," "account-based marketing," and even "personalization" become buzzwords that marketers can throw around without actually incorporating them deeply into their marketing strategies. "Machine learning" is on its way to becoming one of those buzzwords too. Marketers are talking about it a lot and working out the best way to implement it in their marketing strategies, but many are not truly using it in a way that has a real impact on their business.

The world of marketing is no stranger to buzzwords. While many marketing buzzwords words start off as real strategies or tactics that can improve a company's marketing efforts, their meanings often become diluted the more they are used. Over the years, we've seen words like "sales enablement," "social media," "account-based marketing," and even "personalization" become buzzwords that marketers can throw around without actually incorporating them deeply into their marketing strategies. "Machine learning" is on its way to becoming one of those buzzwords too. Marketers are talking about it a lot and working out the best way to implement it in their marketing strategies, but many are not truly using it in a way that has a real impact on their business.

In this paper we present several methods for collecting Web textual contents and filtering noisy data. We show that knowing which user publishes which contents can contribute to detecting noise. We begin by collecting data from two forums and from Twitter. For the forums, we extract the meaningful information from each discussion (texts of question and answers, IDs of users, date). For the Twitter dataset, we first detect tweets with very similar texts, which helps avoiding redundancy in further analysis. Also, this leads us to clusters of tweets that can be used in the same way as the forum discussions: they can be modeled by bipartite graphs. The analysis of nodes of the resulting graphs shows that network structure and content type (noisy or relevant) are not independent, so network studying can help in filtering noise.