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    Machine learning, deep learning, and cognitive computing

    Artificial Neural Network (ANN)

    In machine learning, a neural network is one of the main tools which are somewhat similarly designed like the biological neural system. These consist of neural networks that are used for carrying information, and algorithms that are connected by these networks. Neural networks have a wide variety of applications, and they are considered to be the first step towards the creation of Artificial Intelligence. 

    There are many types of neural networks, some of which include Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and others. The artificial neural network is comparatively newer than others and has its applications in many fields. It can be used in problems related to AI research, and it can be applicable in many fields, some of which include medicine, entertainment, and others (Salah et al., 2018). It can also be used in self-driving cars, and for rendering faces that are made up with the use of Computer Graphics Imagery (CGI).

    Artificial and biological neural networks

    Artificial Neural Network (ANN) was developed to mimic the biological neural network, and their working is almost similar as well. But, there is a vast difference between the two. The human neural network consists of about eighty-six neurons and about a trillion connections that are present between the neurons. In contrast, an artificial neural network contains much less than that, with a maximum of ten to thousand neurons within them. The number of connections that is present between two layers of artificial neurons in the human brain is also much higher than the artificial neural network. The biological neural network can regenerate up to a certain point and is resistant to faults. However, this is not the case with ANN.  

    ANN architectures

    There are different types of architectures of ANN that are present, each of them having their won field of applications. Some of the most commonly available ANN architecture includes feedforward, associative memory, Kohonen's self-organizing feature maps (SOM), Hopfield, and recurrent networks. SOM is capable of classifying data on its own, without any human supervision. Hence, it can be used in cases that involve arbitrary numbers. Hopfield’s network can be used for solving complex problems. 

    Use of ANN in supervised and unsupervised learning

    ANN can be used for both supervised and unsupervised learning. During a supervised learning session, the inputs vector is provided to the network by the user. This input vector will produce an output vector. The produced output vector is then compared with the desired or predicted output vector. An error signal will be raised if any differences are found between the produced vector and the desired vector. In unsupervised learning, similar input vectors are combined to form a cluster. After this, the network is fed with an input pattern, and the network produces an output pattern. This given output identifies the class in which the provided input pattern belongs (Dike et al., 2018). 

    Machine learning methods for a given problem

    In the given situation, a machine learning algorithm and its various models are used to design systems in healthcare. Various models of machine learning which are used for and deployed in this situation are discussed. Mostly the type of machine learning methods used in this case include supervised learning, and are used in applied computing and learning methodologies. Recent advances to address the challenges in healthcare are also covered in this article (Ahmad, Eckert & Teredesai, 2018). 

    Deep learning

    Deep learning is one of the recent trends in AI, which mainly uses mathematical algorithms to learn from the given data set. Like all other machine learning methods, deep learning imitates the human way of learning. However, deep learning is different from other machine learning methods due to its ability to acquire features that help it in solving tasks that are complex (Min, Lee & Yoon, 2017). 

    Paradigms in AI

    Artificial Networks or AI are in a way designed to mimic the way a human brain thinks. It consists of multiples neurons that are grouped and are present in layers that are connected. One of the most common paradigms of these networks is backpropagation. In this, the weights of the neurons are adjusted by using the data from the previous adjustment. Using this, the weights are fine-tuned, and chances of errors are reduced. 

    Representation learning in machine learning and deep learning

    Representation learning or feature learning is the set of techniques that allows the system to discover representations. These representations help in detecting features of raw data, from which the data can be classified. Feature learning makes the classification of inputs easier, as the machine learning process relies on manual inputs to classify a given set of data. However, real-world data such as images, video clips, or data from sensors cannot classify data as such from the given input. In these cases, feature learning is used. 

    ANN activation functions

    Activation functions in ANN are the output of the node at a given input, or a set of inputs that have been provided by the user (Ertuğrul, 2018). In a standard digital circuit, the activation function can either be ON or OFF depending on the input. The ON function is indicated by a 1, and the OFF function is indicated by a 0.


    MLP stands for Multilayer Perceptron. It is a class of feedforward artificial network which consists of three layers of nodes, input-output, and a hidden layer (Ramchoun et al., 2016). The hidden layer has no contact with the external layer and is present internally. The presence of one or more than one of these layers enables stronger computation. MLP has linear activation functions in all the neurons. The weighted inputs are mapped with the output of each neuron. This is done by using a linear function.

    Applications of cognitive computing

    Cognitive computing can be used for the analysis of patterns. It uses computerized models, which are designed to think like a human in a situation where answers might not be certain or unambiguous. IBMs computer system called Watson is closely associated with this phrase. Cognitive computing can be used for data analysis in various industries that handle a huge amount of data. For example, physicians can analyze patient data using cognitive computing in the healthcare industry. Other applications of this technology include improved customer services, and making business processes easier by solving critical issues in real-time.

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