Artificial neural network

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 important to develop accurate cash flow forecast. Statistical, mathematical and simulation approaches were adapted to the identification of risk impact assessment. Though significant variation is still observed with actual and forecasted cash flow. So…

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…

2013; Jaffari et al. 2013; Shahabi et al. 2014; Regmi et al. 2014; Youssef et al. 2015; Karimi Sangchini et al. 2015) were applied for LSM. Also, probabilistic models such as Dempster-Shafer, weights-of-evidence, and Certainty Factor (Mohammady et al. 2012; Pourghasemi et al. 2012; Ozdemir & Altural 2013; Devkota et al. 2013; Pourghasemi et al. 2013; Dou et al. 2014; Youssef et al. 2015) were used to map landslide susceptibility in different countries. Recently, in many sciences and engineering…

came 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…

Neural Networks are networks made of simulated neurons and neuron layers designed to process and evaluate data. Simulated neurons are individual receptors that receive inputs. After receiving an input the neurons process and evaluate it. Following their evaluation of the input the neurons send an output to another simulated neuron in the next neuron layer. Neuron layers are layers of simulated neurons. The simulated neurons are grouped by what type of input they receive and output they produce.…

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…

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…

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…

According to Solso (2008), artificial intelligence is a branch of computer science that uses programs to enhance cognitive functions. Pattern-recognition uses previous experience to identify a pattern accurately. Pattern-recognition through artificial intelligence is an area being 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…

the course, an evaluation progress is generated showing the improvement a child has undergone. Literature Review: Machine Learning techniques have been applied for detection of Learning Disability in students since a long time.Artificial neural network (ANN) and support vector machine (SVM) classification techniques gained a lots of attention within AI world, but never appeared to be used for diagnosis of kids with LDs. Several experimental runs demonstrated that SVN results were consistent…