A Wireless Sensor Network is a network of numerous wireless sensor nodes where every node is equipped with capability of receiving and forwarding the data to the other nodes by using radio frequency. The sensor nodes receive data packets from different nodes and forward these packets towards a central location called sink (Base station). These nodes consume energy for sensing, computations and ...

a local scouring phenomenon is one of the important problems in hydraulic design of groynes. due to constriction and downward flow, the scouring can occur around the groynes. nowadays, the artificial neural networks have a lot of applications in various water engineering problems where there is not any specific relation between effective parameters. in this study, the artificial neural networks...

Shortcut connections are a popular architectural feature of multi-layer perceptrons. It is generally assumed that by implementing a linear sub-mapping, shortcuts assist the learning process in the remainder of the network. Here we nd that this is not always the case: shortcut weights may also act as distractors that slow down convergence and can lead to inferior solutions. This problem can be a...

In this paper, we propose a weakly supervised Restricted Boltzmann Machines (WRBM) approach to deal with the task of semantic segmentation with only image-level labels available. In WRBM, its hidden nodes are divided into multiple blocks, and each block corresponds to a specific label. Accordingly, semantic segmentation can be directly modeled by learning the mapping from visible layer to the h...

A feed-forward multilayer neural net is trained to learn the correspondence between seismic data and well logs. The introduction of a virtual input layer, connected to the nominal input layer through a special nonlinear transfer function, enables ultrafast (single iteration), near-optimal training of the net using numerical algebraic techniques. A unique computer code, named DeepNet, has been d...

The arrival of the big data era with characteristics such as large volumes makes calculation execution time a concern when carrying out analytics processes, forecasting food commodity prices. This study aims to examine effect framework through use sparkR. test is carried by varying several deep learning models, namely multi-layer perceptron model and using price one from 2018 2020. results show...

This report provides detailed description and necessary derivations for the BackPropagation Through Time (BPTT) algorithm. BPTT is often used to learn recurrent neural networks (RNN). Contrary to feed-forward neural networks, the RNN is characterized by the ability of encoding longer past information, thus very suitable for sequential models. The BPTT extends the ordinary BP algorithm to suit t...

In a mesh network, each node acts as a router/repeater for other nodes in the network. These nodes can be fixed pieces of network infrastructure and/or can be the mobile devices themselves. In such networks, because of the heterogeneous transmission range of the clients and routers, link asymmetry problem exists. Link asymmetry poses several challenges such as the unidirectional link problem, t...

in this paper, the grouting ability of sandy soils is investigated by artificial neural networks based on the results of chemical grout injection tests. in order to evaluate the soil grouting potential, experimental samples were prepared and then injected. the sand samples with three different particle sizes (medium, fine, and silty) and three relative densities (%30, %50, and %90) were injecte...