Neural network

As Paul Merolla wrote in their research, “A long standing dream has been to harness neuroscientific insights to build a versatile computer that is efficient in terms of energy and space, homogenously scalable to large networks or neurons and synapse, and flexible enough to run complex behavioral models of the neocortex as well as networks inspired by neural architecture (688).” Neural Networks (Artificial Intelligence) vs. Human Brain Neural Networks A neural network or artificial neural network, is an artificial intelligence system capable of finding and differentiating patterns. According to Sergey Lobov, “Artificial neural networks (ANN) represent one of the effective tools currently used for pattern recognition in many applications, both as classifiers and for dimension reduction. (27895)” The human brain acts in a similar manner as a neural network, as it has learned to consider factors in combination with recognizing and differentiating objects. (Haag, Cummings 111) Neuroscientist Christof Koch proclaimed that, “Any creature vastly outperforms any machine today” (qtd in Watson). If creatures can outperform any machine today, could a technology be developed that mimics how the human brain…

An Artificial neural network model for risk impacts on cash flow forecast in construction industry Key Words: Risk Factors, Risk Impacts, Model, Artificial Neural Network, Cash Flow Forecast Area of Research Cash flow forecasting is a vital contributing factor in construction industry where lead to the high rate of insolvencies. Risks involved with construction industry play significant role for the variation of forecasted and actual cash flow. Identification of risks and risk assessment are…

The use of neural networks in fuzzy modelling and control systems which are built on fuzzy logic has made possible the creation of the so-called neuro-fuzzy systems. A neuro-fuzzy system is a modified artificial neural network, meaning that this system contains a fuzzy system and more memory storage capacities than a simple neural network. Fuzzy systems are considered to be more friendly than neural networks because their behaviour can be explained using the human reasoning model. The main…

This is the most popular network architecture in use today. The input layer of this network is a set of input units, which accept the elements of input feature vectors. The input units (neurons) are fully connected to the hidden layer with the hidden units. The hidden units (neurons) are also fully connected to the output layer. The output layer supplies the response of neural network to the activation pattern applied to the input layer. The information given to a neural net is propagated…

Kelly Neuroth’s oral presentation discussing Artificial Intelligence in Power Systems was very effective in its delivery. While shorter than expected, the discussion efficiently introduced how artificial intelligence is being used in high output power plants to detect cracks. He then explained neural networks and how computers can be trained with photos of cracks, scratches and other defects. Using these photos, the neural network learns to tell the difference between cracks and not cracks.…

BC5 CHILD DEVELOPMENT AND COGNATION LEARNING II M2014EE007_HARSHILA Briefly scan the science, mathematics engineering education literature. Locate and describe a misconception/ alternative conception around a topic discussed in literature (such as Earth being flat Vs. the ‘correct’ conception of it being round) exhibited among children/students. Discuss this misconception and its nature in relation to networks perspective as well as Gibson’s idea of “affordance”. Try to build a discussion along…

It is worth mentioning that prediction of the concrete compressive strength is an important fact in the quality assurance of the produced concrete. Although there are numerous methods of predicting the mechanical properties of concrete, not all of them are valuable since they are in accordance with many trial and errors. As mentioned earlier, three different models of multiple linear regressing (MLR), artificial neural network (ANN), and ANFIS are used to reach the goal in this study. These…

to know about the application of Artificial Neural Network and Machine Learning in image processing. I opted for ANN course in my third year. I was amazed by the wide ranging application of the ANN in other fields. During this course I came to know about different network models that have been inspired by human brain. I realized that making things by taking inspiration from nature is very efficient in compare with things made from scratch. Under the guidance of Prof G.N Pillai, I completed a…

characteristic was possessed by not just humans, but other, inorganic life? Machines can think, and we have made them so. A system of neural networks is used to estimate functions that depend on a large number of unknown inputs. Neural networks are labeled the tripe counterpart to AI, Artificial Intelligence. This technology is the intellectual ability exhibited by machines. It has flexible rational agents that perceives its environment or situation, and takes actions that maximize its chance…

explored at an extraordinary rate. Through the use of experiments using mouse brains and three data sets that consider neural networking for pattern recognition is not so far away. In the study presented by Zeng (2015), mice brains were analyzed for the gene expression patterns. This study used the Allen Developing Mouse Brain Atlas (ADA) and Allen Developing Mouse Brain Reference Atlas (ARA) along with in-situ hybridization (ISH) gene expression patterns…