Artificial Neural Networks ( Anns )

The objective of the neural network is to transform the input to meaningful output. Neural networks are often used for statistical analysis and data modeling. Neural network has many uses in data processing, robotics, and medical diagnosis [2]. From the starting of the neural network there are various types found, but each and every types has some advantages and disadvantages. Deep learning and -neural network software are the categories of artificial neural network. The parallel process also allows ANNs to process the large amount of data very efficiently. The artificial neural network is built with a systematic

Understanding the way A.I. works is crucial to understanding A.I. goals. There are several traits that separate Artificial Intelligence from regular machines. One such trait is an A.I.’s ability to “think” through Neural Networks, which are networks made of simulated neurons and neuron layers designed to process and evaluate data. The simulated neurons are individual receptors designed to process and evaluate inputs. Following their evaluation, the neurons send an output to another simulated neuron in the next neuron layer. These neuron layers are layers of simulated neurons grouped by what type of input they receive and output they produce, that scales in complexity (Knight). MIT tech review senior editor Will Kight provided the example of

One of the biggest advantages of Neural Network is that it can actually learn from observing data sets. This way it uses a random function approximation tool, which helps to estimate the most efficient and ideal solution while defining all the computing functions and distributions. Neural Networks takes data samples instead of entire data sets to arrive to a solution, which saves a lot of time and money. Neural Networks are considered as simple mathematical models to enhance existing data analysis technology.

In Engineering, there are 5 main concepts and methods of Artificial Intelligence which are: Knowledge-based systems, Neural networks, Genetic algorithms, Fuzzy logic, and Intelligent agents. In Knowledge-based systems, in order to solve a problem, the problem solver must have a substantial amount of knowledge prior to the problem (Rzevski 6). In the article “Artificial Intelligence in Engineering: Past, Present, and Future” by George Rzevski, Feignbaum states that the performance of this system relies on the amount of information stored. In Neural networks, they allow researchers to learn from examples, store information that’s organized well, and recognize partially specified patterns in engineering applications. As a result, problems could be solved through pattern recognition. In Genetic algorithms, they are

According to Dr. R.J Wicks who is a leading data scientist in the field of artificial intelligence,

The theories around AI and machine learning brings new dangers. Specifically, machine learning frameworks frequently have low " interpretability," implying that people experience problems making sense of how the structures achieved

Artificial intelligence techniques are increasingly enriching decision support through means as data delivery, analyzing data trends, providing forecasts, developing data consistency, information providing to the exploiter in the most appropriate forms and suggesting courses of action.

This is an empirical example which has the role of highlighting the differences between a neural network and a linear regression model.

A proper activation function significantly improves the performance of a deep neural network. Rectified linear unit proposed by (ReLU) (Nair and Hinton 2010) is one of the most widely used activation functions. The ReLU activation function is defined as: f(x) = max(x,0). Effectively, ReLU is a linear function that prunes the negative part to zero and retains the positive part as is. Intuitively, ReLU avoids the vanishing gradient problem by setting the positive part to identity. (Krizhevsky et al., 2012) showed that that deep networks can be efficiently trained using ReLU even without pre-training. Compared to tanh and sigmoid neurons, that involve expensive operations, the ReLU can be implemented by simply

SmolinskiG., MilanovaM. G. and HassanienA. E.: Studies in Computational Intelligence :Computational Intelligence in Biomedicine and Bioinformatics. ISBN-10: 354070776X | ISBN-13: 978-3540707769,Springer-Verlag(2011).

Most experts agree that Artificial Intelligence is on the increase in various fields. It has become one of the most controversial scientific issues of the twenty-first century. It raises the unemployment rate, workers will not find enough jobs for them and some of them may lose their work. As a result of technology, according to Wilson, Daugherty and Bianzino (2017), "The threat that automation will eliminate a broad swath of jobs across the world economy is now well established. As Artificial Intelligence (AI) systems become ever more

First of all, we have encountered one major problem that is how to interpret a neural network given its black box characteristics. We really wanted to try ourselves giving interpretation to our results so that we dug into the existing literature and found out a very interesting research paper written by Garson in 1991. In « Illuminating the black box: a randomization approach for understanding variable contributions in artificial neural networks », Olden and al. describes Garson’s algorithm very concisely so that we were able to create a user-defined function on Python that replicates the method. The interpretation of the method is provided below.

These stages represent the network from input to pooling, pooling to pooling, and pooling to output layers. Using this proposal, CNNs evolved by the GA resulted in very different architectures when compared with hand-designed ones, but often with a better performance. However, a major disadvantage of the proposed framework is the training process, which has to be done from scratch for every evolved new architecture. Fixed-size convolutional blocks also limit the proposed method by not incorporating multi-scaling analysis, and this issue will be present in most of the direct encoded methods designed for this task.

Convolutional neural systems (CNNs) are suitable for unraveling visual record that depend on hand writing recognition task and characterization [1, 3]. They have an adaptable design which do not need to have complex strategies, for instance, momentum, weight rot, structure dependent learning rates or even finely tuning the engineering[1]. CNNs have additionally accomplished the cutting edge comes about for character acknowledgment on the MNIST informational collection of manually written English digit pictures [2].

Big data analysis is one of the most important orientations in the area of information technology because of its importance in extracting information out of the available huge digital data and because of the increasingly growing use of internet services and digital transactions of the internet. In addition, data analysis plays a vital role in developing the future generation of products and services of intelligent systems for many different purposes. The National Center for Computation Technology and Applied Math CTAM deploys most of its research groups (areas of artificial intelligence, data mining, machine learning, high performance computing, language processing, software engineering) to lead national initiatives that focus on data science