{"id":11648,"date":"2022-02-07T00:00:00","date_gmt":"2022-02-07T00:00:00","guid":{"rendered":"https:\/\/www.nextias.com\/current_affairs\/uncategorized\/07-02-2022\/artificial-neural-networks\/"},"modified":"2022-02-07T00:00:00","modified_gmt":"2022-02-07T00:00:00","slug":"artificial-neural-networks","status":"publish","type":"post","link":"https:\/\/www.nextias.com\/ca\/current-affairs\/07-02-2022\/artificial-neural-networks","title":{"rendered":"Artificial Neural Networks"},"content":{"rendered":"<p><span style=\"font-size:13pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong><u>In Context<\/u><\/strong><\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Artificial Neural Networks<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"> are an important breakthrough in Artificial Intelligence &#038; Deep Learning.\u00a0<\/span><\/span><\/span><\/li>\n<\/ul>\n<p><span style=\"font-size:13pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong><u>About Artificial Neural Networks<\/u><\/strong><\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Neural networks<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"> are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>An ANN is based on a collection of connected units<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"> or nodes called <\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>artificial neurons<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">, which loosely model the neurons in a biological brain.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Each connection<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">, like the synapses in a biological brain, can transmit a signal to other neurons.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">An<\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong> artificial neuron<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"> receives a signal then processes it and can signal neurons connected to it. The &#8220;signal&#8221; at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs.<\/span><\/span><\/span>\n<ul>\n<li style=\"list-style-type:circle\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">The connections are called <\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>edges.<\/strong><\/span><\/span><\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Back propagation<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">: A feedback or difference between intended output and the input is computed at each layer and this difference is used to tune the parameters to each program.\u00a0<\/span><\/span><\/span>\n<ul>\n<li style=\"list-style-type:circle\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">This method is called<\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong> back propagation<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"> and it is an essential component to the Neural Network.<\/span><\/span><\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p style=\"text-align:center\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><img decoding=\"async\" src=\"https:\/\/lh3.googleusercontent.com\/5tWBodLrHb-UFvoTuTVnI_93OxNrNnR7aaJ09Ua82laYd9v4vNasIIrW81Fe0a715F77D73TvLAZyQp5p-UayiqWRgUQ3wVrYFVSFSQi94U2lX1OpyPb-8h5JhAM08UOt55vz8aF\" style=\"height:341px; width:425px\" \/><\/span><\/span><\/span><\/p>\n<p><span style=\"font-size:13pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong><u>Advantages of Artificial Neural Networks (ANN)<\/u><\/strong><\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Neural networks help us cluster and classify<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">: You can think of them as a clustering and classification layer on top of the data you store and manage.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Neural networks can also extract features<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"> that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Storing information on the entire network: <\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">Information such as in traditional programming is stored on the entire network, not on a database. The disappearance of a few pieces of information in one place does not prevent the network from functioning.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Ability to work with incomplete knowledge<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">:\u00a0 After ANN training, the data may produce output even with incomplete information. The loss of performance here depends on the importance of the missing information.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Having fault tolerance<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">:\u00a0 Corruption of one or more cells of ANN does not prevent it from generating output. This feature makes the network fault tolerant.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Having a distributed memory<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">: In order for ANN to be able to learn, it is necessary to determine the examples and to teach the network according to the desired output by showing these examples to the network.<\/span><\/span><\/span>\n<ul>\n<li style=\"list-style-type:circle\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">The network&#8217;s success is directly proportional to the selected instances, and if the event cannot be shown to the network in all its aspects, the network can produce false output<\/span><\/span><\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Gradual corruption:<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">\u00a0 A network slows over time and undergoes relative degradation. The network problem does not immediately corrode.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Ability to make machine learning<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">: Artificial neural networks learn events and make decisions by commenting on similar events.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Parallel processing capability<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">:\u00a0 Artificial neural networks have numerical strength that can perform more than one job at the same time.<\/span><\/span><\/span><\/li>\n<\/ul>\n<p><span style=\"font-size:13pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong><u>Disadvantages of Artificial Neural Networks (ANN)<\/u><\/strong><\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Hardware dependence<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">:\u00a0 Artificial neural networks require processors with parallel processing power, in accordance with their structure. For this reason, the realization of the equipment is dependent.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Unexplained behavior of the network<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">: This is the most important problem of ANN. When ANN produces a probing solution, it does not give a clue as to why and how. This reduces trust in the network.\u00a0\u00a0<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Determination of proper network structure<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">:\u00a0 There is no specific rule for determining the structure of artificial neural networks. Appropriate network structure is achieved through experience and trial and error.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Difficulty of showing the problem to the network<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">:\u00a0 ANNs can work with numerical information. Problems have to be translated into numerical values before being introduced to ANN. The display mechanism to be determined here will directly influence the performance of the network. This depends on the user&#8217;s ability.<\/span><\/span><\/span><\/li>\n<li style=\"list-style-type:disc\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>The duration of the network is unknown<\/strong><\/span><\/span><\/span><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\">: The network is reduced to a certain value of the error on the sample means that the training has been completed. This value does not give us optimum results.<\/span><\/span><\/span><\/li>\n<\/ul>\n<p><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong>Source: <\/strong><\/span><\/span><\/span><a href=\"https:\/\/www.thehindu.com\/sci-tech\/technology\/understanding-artificial-neural-networks\/article38390874.ece#:~:text=The%20concept%20behind%20an%20Artificial,until%20a%20result%20is%20obtained\" style=\"text-decoration:none\" target=\"_blank\" rel=\"noopener\"><span style=\"font-size:12pt\"><span style=\"font-family:'Book Antiqua',serif\"><span style=\"color:#000000\"><strong><u>TH<\/u><\/strong><\/span><\/span><\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In Context Artificial Neural Networks are an important breakthrough in Artificial Intelligence &#038; Deep Learning.\u00a0 About Artificial Neural Networks Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":11649,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21],"tags":[26,33],"class_list":["post-11648","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-current-affairs","tag-gs-3","tag-science-technology"],"acf":[],"jetpack_featured_media_url":"https:\/\/wp-images.nextias.com\/cdn-cgi\/image\/format=auto\/ca\/uploads\/2023\/07\/4055243Screenshot_6.png","_links":{"self":[{"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/posts\/11648","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/comments?post=11648"}],"version-history":[{"count":0,"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/posts\/11648\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/media\/11649"}],"wp:attachment":[{"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/media?parent=11648"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/categories?post=11648"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.nextias.com\/ca\/wp-json\/wp\/v2\/tags?post=11648"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}