Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happen to enable organizations some space to act on the prediction. There are some really good state of the art machine learning techniques to optimally identify the possible churners in either customer base or employee base, similarly for fault prediction too if the prediction does not come with some buffer time to act on the fault it is very difficult to provide a seamless experience to the user. New concept of reference frame creation is introduced to solve this problem in this paper

Artificial Intelligence (AI) and Machine Learning (ML) are changing everything in the electronics industry. Engineers are now evaluating how to design and train intelligence solutions in everything from sensors, to smartphones, to networks, to cloud data centers. However, just as there has never been a single processor for every application or workload, so too is there no single solution for AI. Qualcomm appears to be hedging its bets with a distributed AI solution the company calls the Artificial Intelligence Engine (AIEngine) and a dedicated AI accelerator, that was just announced at a company sponsored event in China. At the moment, most of the training of artificial neural networks is done in data centers.

Paul Steinberg, CTO of Motorola Solutions, speaks to Sam Fenwick about his company's efforts to use AI and machine learning to bring the right data to the user in the right way Paul Steinberg presides over a huge range of research and development activities, ranging from RF engineering and wireless network architectures to drones and robotics. He also manages Motorola Solutions Venture Capital's portfolio and plays a key role in managing Motorola Solutions' intellectual property. One of the things the company is moving towards is a virtual partner – a combination of AI and natural language processing, which allows someone in the field to verbally request information and give commands without talking to a human. Part of the thinking behind this is that people speak faster than they can type, and the need for field workers to stay aware of their surroundings. "The way you and I consume [mobile data] is a slab of black glass, [but the] fundamental imperative [for a police officer, etc] is eyes-up, hands-free. That slab of black glass [is] exactly the opposite: eyes-down, hands-busy. A big part of how we're navigating this problem is around ethnographics and human factors research – living a day in the life of our users and then [working] with the technologists and designers."

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Expensive flagship phones won't be the only way for you to play with advanced features like AR Emoji, Animoji and Face ID much longer. Qualcomm is making it easier for companies to create mid-range smartphones that pack those functions by launching a new mobile processor. The Snapdragon 710 will come with a multi-core AI Engine and support neural network processing, as well as image signal processors and graphics units that are typically found in higher-end chipsets. The 710 is the first of the 700-series, which was announced at MWC this year, and will sit above options like the 600- and 400-ranges but below top-tier chips like the Snapdragon 845. The Snapdragon 710 is a 10nm chipset that features a multi-core AR engine for on-device neural networking processing, as well as a Spectra 250 image signal processor that enables things like multi-frame noise reduction and AI camera features like video style transfer and active depth sensing for artificial bokeh.

In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resources to wireless systems. To ensure a conflict-free resource utilization, it is useful to predict the prospective medium utilization before resources are allocated. This paper presents a self-learning concept, which is based on reinforcement learning. A simulative evaluation of reinforcement learning agents based on neural networks, called deep Q-networks and double deep Q-networks, was realized for exemplary and practically relevant coexistence scenarios. The evaluation of the double deep Q-network showed that a prediction accuracy of at least 98 % can be reached in all investigated scenarios.

The last ten years have seen radical changes in the cable television sector. Market forces, new technologies, and consumer demand have remade cable companies as telecommunications giants handling bundled services. Chances are that streaming content and upstart players will continue to remake the industry, and we'll see even more new models and technologies emerge over the next ten years. Jeanine Heck is one woman driving this dramatic shift at Comcast. If you're a Comcast customer, then chances are you're already using a product she was responsible for bringing to life: the voice remote.

Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms). We show strong generalizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.

The Mobile Virtual Network Operator (MVNO) business model first emerged in Japan in 1997. Since then, the global MVNO subscriber base has steadily grown and is expected to soon exceed the 300-million landmark. It is currently growing five times faster than the operator segment. The MVNO business market has however, always been controversial. Despite its success, many MVNOs struggle financially and many fail a few months after their much-hyped launch.

As pop-up stores around the world put the latest OnePlus handset on sale today, and the rest of the world's retail stores put it on sale tomorrow, what does the world's press think of the OnePlus 6? The notch is on show, the dual lens camera is tested, and the all-glass construction help it stand out. is that enough? OnePlus is leaning heavily on "The Speed You Need" as the key marketing phrase, and the specifications bear that out. Much like many high-end handsets, the OnePlus 6 comes with a Qualcomm Snapdragon 845, paired up with 6gB or 8GB of RAM, and internal storage options of 64GB, 128GB, or 256GB. I still think that getting the 8GB RAM model is unnecessary for most customers, and the Pixel 2 XL shows that there is still room for improvement when it comes to smoothness.