Deep multi-layer neural networks have many levels of non-linearities allowing them to compactly represent highly non-linear and highly-varying functions. However, until recently it was not clear how to train such deep networks, since gradient-based optimization starting from random initialization often appears to get stuck in poor solutions. Hinton et al. recently proposed a greedy layer-wise u...

Image enhancement plays an important role in computer vision. The degraded image, blurred image and noised image effect the medical diagnosis of image data, satellite image for information retrieval. Various authors and researcher propose a method of image enhancement such as histogram equalization, multi-point histogram equalisation and some method based on neural network and wavelet threshold...

Abstract Cloud computing, which is distributed, stored and managed, drawing attention as data generation storage volumes increase. In addition, research on green increases energy efficiency, also widely studied. An index constructed to retrieve huge dataset efficiently, the layer-based indexing methods are used for efficient query processing. These construct a list of layers, so that only one l...

Security is a mandatory issue in any network, where sensitive data are transferred safely the required direction. Wireless sensor networks (WSNs) formed hostile areas for different applications. Whatever application, WSNs must gather large amount of and send them to an authorized body, generally sink. WSN has integrated with Internet-of-Things (IoT) via internet access nodes along internet-conn...

Analog in-memory computing (AIMC) has been utilized in convolutional neural networks (CNNs) edge inference engines to solve the memory bottleneck problem and increase efficiency. However, AIMC analog-to-digital converters (ADCs) restricted resolution imposes quantization of output activations that can reduce accuracy without meticulous optimization. A study conducted calibration obtained config...

Supervised training of multi-layer perceptrons (MLP) with only few labeled examples is prone to overfitting. Pretraining an MLP with unlabeled samples of the input distribution may achieve better generalization. Usually, pretraining is done in a layer-wise, greedy fashion which limits the complexity of the learnable features. To overcome this limitation, two-layer contractive encodings have bee...

The deep Boltzmann machine is a powerful model that extracts the hierarchical structure of observed data. While inference is typically slow due to its undirected nature, we argue that the emerging feature hierarchy is still explicit enough to be traversed in a feedforward fashion. The claim is corroborated by training a set of deep neural networks on real data and measuring the evolution of the...

Mannose is an abundant cell surface monosaccharide and has an important role in many biochemical processes. It binds to a great diversity of receptor proteins. In this study we have employed Random Forest for prediction of mannose binding sites. Mannosebinding site is taken to be a sphere around the centroid of the ligand and the sphere is subdivided into different layers and atom wise and resi...