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Can we predict Traffic Flow Using Neural Network?

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Ongoing traffic volume expectation assumes a fundamental job in proactive system the executives, and many estimating models have been proposed to address this issue in the writing. Notwithstanding, the greater part of them experience the ill effects of the powerlessness to completely utilize the rich data in rush hour gridlock information to produce proficient and precise traffic expectations for a more drawn out term (i.e., 7-day forecasts at a 5-min interim). We center around anticipating multi-step continuous traffic volume utilizing two kinds of Long Short-Term Memory (LSTM) systems: many-to-one LSTM and many-to-numerous LSTM by making an adaptable group guaging framework that consolidates quantities of neural system and forecasts out of interjection. Considering the enormous measure of information focuses in the current dataset, so as to address one of the run of the mill concerns we have with RNN models regarding longer preparing time, we do examining on the first datas
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Does Spinal Muscle Antropy Disease Classified Using Machine Learning?

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A genetic issue is an acquired issue brought about by at least one irregularities formed in the genome. Most inherited issue are generally uncommon and affect one individual in each various thousands or millions issue might be heritable, implying that they are affirmed down from the guardians' qualities. Muscle decay is characterized as a decrease in the mass of the muscle; it very well may be a fragmented or complete slaughtering ceaselessly of muscle, and is most typically qualified when people endure passing impairing situation, for example, being compelled in development as well as controlled to bed as when hospitalized. Spinal solid decay is a hereditary issue described by shortcoming and squandering (decay) in muscles utilized for development (skeletal muscles). It is brought about by lost specific nerve cells, considered engine neurons that control muscle development. ... They have massively powerless muscle tone (hypotonia) during childbirth. Spinal strong decay (
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Pointer Networks: An Introduction

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Pointer networks are a variation of the sequence-to-sequence model with attention. Instead of translating one sequence into another, they yield a succession of pointers to the elements of the input series. The most basic use of this is ordering the elements of a variable-length sequence or set. Basic seq2seq is an LSTM encoder coupled with an LSTM decoder. It’s most often heard of in the context of machine translation: given a sentence in one language, the encoder turns it into a fixed-size representation. Decoder transforms this into a sentence again, possibly of different length than the source. For example, “como estas?” - two words - would be translated to “how are you?” - Three words. The model gives better results when augmented with attention. Practically it means that the decoder can look back and forth over input. Specifically, it has access to encoder states from each step, not just the last one. Consider how it may help with Spanish, in which adjectives go before
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Journey of Artificial Intelligence:

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"Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems." In the 50s, we started to see SQL databases with only one type of format. Then moving along, larger databases such as Oracle and Informix appeared. I started to use Database as a relevant tool in the early 80s. Today the variety of formats like pictures, videos, texts, engineering data, spreadsheets, mobile data, social media and emails require a different database format. This is why NoSQL started to exist (not only SQL). Data and the amount of data are more and more available. The intensity of data also started to become clear in the mid of 50s. However, at that time we did not know how to gather data and more importantly how to use data. Today, data has great value to all companies. Until recently, we had never thought of putting together all the different types of data. Over time companies have started to realize that they could use more and
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Will One Small Step for AI Be One Giant Leap for Robotics?

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Robot learns to walk by itself using artificial intelligence. Have you ever wondered how human-like a robot can become? Initially, in the learning phase, a tendon-driving robotic limb undergoes a motor babbling phase where the system attempts random control sequences and gathers the associated kinematics. The input-output data from the motor babbling is fed to a multi-layer perceptron artificial neural network (ANN) to train it. In turn, the trained ANN produces an initial output-input (inverse) map based on the system’s dynamics. The ANN of the inverse map from 6D kinematics to a 3D motor control sequences has three layers and twenty-four nodes total. There are six nodes in the input layer, fifteen nodes in the hidden layer, and three nodes in the output layer. The hyperbolic tangent sigmoid transfer function was used to compute a layer’s output from its net input—well suited for neural networks when velocity is a priority over the precise shape of the transfer function
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Neuromorphic Computing: The Next Phase of Artificial Intelligence Technologies

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The arms race between competing artificial intelligence technologies will ultimately decide how we address our cyber security challenges. The use of artificial intelligence and machine learning systems is increasing rapidly. ‘ Machine learning ’ describes systems that can learn the correct response simply by analysing lots of sample input data, without having to be explicitly programmed to perform specific tasks. Perhaps the most successful and widespread technique is the use of artificial neural networks (ANNs). ANNs copy the manner in which that neurons work in organic frameworks, for example, the human cerebrum, making a system of interconnected counterfeit neurons. They have demonstrated to be compelling at various errands, particularly those including design acknowledgment, for example, PC vision, discourse acknowledgment or therapeutic determination from side effects or outputs. The most-used tool in the cybercriminal’s toolbox is the DDoS, or distributed deni
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Is Deep Learning Just Neural Networks on Steroids?

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Is " Deep Learning " simply one more name for advanced neural systems, or is there more to it than that? We take a look on going advance in deep learning just as neural systems. Neural systems are more mind complex than only a multiplayer perception; they can have a lot progressively concealed layers and even intermittent associations. Yet, hang tight, don't despite everything they utilize the back engendering calculation for preparing? Anyway, if the idea isn't new, would this be able to imply that deep learning is only a cluster of neural systems on steroids? Is the entire object just because of parallel calculation and all the more powerful machines? Regularly, when I analyse supposed profound learning arrangements, this is what it resembles. When burrowing somewhat deeper, we do locate a couple of new units, designs and procedures in the field of deep learning. A portion of these advancements convey a littler weight, similar to the randomization presen
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