Image recognition based on Boltzmann machine algorithm

Due to the influence of recognition parameters, image recognition has low recognition accuracy, long recognition time and large storage cost. Therefore, an automatic image recognition method based on Boltzmann machine is proposed. Based on threshold method and fuzzy set method, image malformation correction is performed. The mean filter and median filter are combined to eliminate the influence of image filtering, and the pre-processing of image is completed by using the fuzzy enhancement of image. Based on the restricted Boltzmann method, the network model is dynamically evolved, and the identification parameters of each shape and contour are obtained. Different shapes and contours are classified and recognized. Simulation results show that image recognition method based on human-computer interaction has high recognition ability, shortens the time cost and greatly reduces the space needed for node storage.

An Improved Kidney Tumor Prediction Using Deep Convolutional Neural Network-Restricted Boltzmann Machine Technique in Medical Image Segmentation

Automatic medical image segmentation has become increasingly important as contemporary medical imaging has become more widely available and used. Existing image segmentation solutions however lack the necessary functionality for simple medical image segmentation pipeline design. Pipelines that have already been deployed are frequently standalone software that has been optimised for a certain public data collection. As a result, the open-source python module deep-Convolutional neural network-Restricted Boltzmann Machine (deep CNNRBM) was introduced in this research work. The goal of Deep CNN-purpose RBMs is to have an easy-touse API that allows for the rapid creation of medical image segmentation transmission lines that include data augmentation, metrics, data I/O pre-processing, patch wise analysis, a library of pre-built deep neural networks, and fully automated assessment. Similarly, comprehensive pipeline customisation is possible because of strong configurability and many open interfaces. The dataset of Kidney tumor Segmentation challenge 2019 (KiTS19) acquired a strong predictor with respect to the standard 3D U-net model after cross-validation using deep CNNRBM. To that purpose, deep CNN-RBM, an expressive deep learning medical image segmentation architecture is introduced. The CNN sub-model captures frame-level spatial features automatically while the RBM submodel fuses spatial data over time to learn higher-level semantics in kidney tumor prediction. A neural network recognises medical picture segmentation, which is initiated using RBM to second-order collected data and then fine-tuned using back propagation to be more differential. According to the simulation outcome, the proposed deep CNN-RBM produced good classification results on the kidney tumour segmentation dataset.

A Boltzmann Machine Optimizing Dynamic Routing for FANETs

Routing optimization for FANETs is a kind of NP-hard in the field of combinatorial optimization that describes simple and difficult to handle. The quality of routing has a direct impact on the network quality of FANETs, and the design of routing protocols becomes a very challenging topic in FANETs. In this paper, we study the characteristics of dynamic routing, combine the characteristics of FANETs themselves, use the energy of nodes, bandwidth, link stability, etc. as the metric of routing, and use Boltzmann machine for routing search to form an optimized dynamic routing protocol. The NS3 simulation simulator is used to compare and study with traditional MANET dynamic routing AODV and DSR, and the simulation results show that the routes obtained by using Boltzmann machine search are better than AODV and DSR in many aspects such as end-to-end average delay, average route survival time and control overhead.

Restricted Boltzmann Machine representation for the groundstate and excited states of Kitaev Honeycomb model

In this work, the capability of restricted Boltzmann machines (RBMs) to find solutions for the Kitaev honeycomb model with periodic boundary conditions is investigated. The measured groundstate (GS) energy of the system is compared and, for small lattice sizes (e.g. 3×3 with 18 spinors), shown to agree with the analytically derived value of the energy up to a deviation of 0.09 %. Moreover, the wave-functions we find have 99.89 % overlap with the exact ground state wave-functions. Furthermore, the possibility of realizing anyons in the RBM is discussed and an algorithm is given to build these anyonic excitations and braid them for possible future applications in quantum computation. Using the correspondence between topological field theories in (2+1)d and 2d CFTs, we propose an identification between our RBM states with the Moore-Read state and conformal blocks of the 2 d Ising model.

Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems

Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The effectiveness of this method in simple systems has been well verified, but its performance is still unknown due to many problems in real communication such as interference from other users and base stations, and users are constantly on the move. In this article, we propose a dynamic user clustering hybrid precoding method in the high-dimensional millimeter-wave multiple-input multiple-output system, which uses low-dimensional manifolds to avoid complicated calculations when there are many antennas. We model each user set as a novel Convolutional Restricted Boltzmann Machine manifold, and the problem is transformed into cluster-oriented multi-manifold learning. The novel Convolutional Restricted Boltzmann Machine manifold learning seeks to learn embedded low-dimensional manifolds through manifold learning in the face of user mobility in clusters. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.

Comparison of quantum and classical methods for labels and patterns in Restricted Boltzmann Machines

Classification and data reconstruction using a restricted Boltzmann machine (RBM) is presented. RBM is an energy-based model which assigns low energy values to the configurations of interest. It is a generative model, once trained it can be used to produce samples from the target distribution. The D-Wave 2000Q is a quantum computer which has been used to exploit its quantum effect for machine learning. Bars-and-stripes (BAS) and cybersecurity (ISCX) datasets were used to train RBMs. The weights and biases of trained RBMs were used to map onto the D-Wave. Classification as well as image reconstruction were performed. Classification accuracy of both datasets indicates comparable performance using D-Wave’s adiabatic annealing and classical Gibb’s sampling.

adabmDCA: adaptive Boltzmann machine learning for biological sequences

Background Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are parametrized in terms of local biases accounting for residue conservation, and pairwise terms to model epistatic coevolution between residues. From the model parameters, it is possible to extract an accurate prediction of the three-dimensional contact map of the target domain. More recently, the accuracy of these models has been also assessed in terms of their ability in predicting mutational effects and generating in silico functional sequences. Results Our adaptive implementation of Boltzmann machine learning, , can be generally applied to both protein and RNA families and accomplishes several learning set-ups, depending on the complexity of the input data and on the user requirements. The code is fully available at https://github.com/anna-pa-m/adabmDCA. As an example, we have performed the learning of three Boltzmann machines modeling the Kunitz and Beta-lactamase2 protein domains and TPP-riboswitch RNA domain. Conclusions The models learned by are comparable to those obtained by state-of-the-art techniques for this task, in terms of the quality of the inferred contact map as well as of the synthetically generated sequences. In addition, the code implements both equilibrium and out-of-equilibrium learning, which allows for an accurate and lossless training when the equilibrium one is prohibitive in terms of computational time, and allows for pruning irrelevant parameters using an information-based criterion.

Restricted Boltzmann Machine based on a Fermi sea

In recent years, there has been an intensive research on how to exploit the quantum laws of nature in the machine learning. Models have been put forward which employ spins, photons, and cold atoms. In this work we study the possibility of using the lattice fermions to learn the classical data. We propose an alternative to the quantum Boltzmann Machine, the so-called Spin-Fermion Machine (SFM), in which the spins represent the degrees of freedom of the observable data (to be learned), and the fermions represent the correlations between the data. The coupling is linear in spins and quadratic in fermions. The fermions are allowed to tunnel between the lattice sites. The training of SFM can be eciently implemented since there are closed expressions for the log- likelihood gradient. We nd that SFM is more powerful than the classical Restricted Boltzmann Machine (RBM) with the same number of physical degrees of freedom. The reason is that SFM has additional freedom due to the rotation of the Fermi sea. We show examples for several data sets.