Training neural networks with universal adiabatic quantum computing.

The purpose of the research, the results of which are presented in this article, is to determine the possibility and evaluate the effectiveness of using a trained neural network in the diagnosis of ringworm. The article provides an analysis of the methods used for diagnosing dermatomycosis in veterinary practice. One of the actively developing areas at present is the use of artificial neural networks in the diagnosis of animal diseases. The authors have developed a method for diagnosing dermatophytosis using a trained neural network. To identify hair damaged by dermatophyte spores in cats, a trained artificial neural network YOLO v5 was used, based on the YOLO architecture (high-precision artificial neural network), which provides high accuracy and speed of object detection in images. Diagnostics was carried out in three stages. The first stage: the diagnosis of hair in cats damaged by dermatophyte spores was carried out using a trained artificial neural network. The second stage: microscopy by a veterinary specialist of the veterinary center. The third stage: comparison of the received data from the trained artificial neural network and veterinary specialists. Three comparative experiments were carried out on 20 depersonalized samples with different ratios from healthy and sick animals. As a result of testing the trichoscopy method using artificial neural networks for diagnosing spore-damaged hair dermatitis in cats, it was found that a trained artificial neural network of 60 studied samples diagnosed dermatophyte spore damage in 20 samples, a veterinarian - in 17. All positive results were confirmed by a mycological laboratory study. and identification of the pathogen. It has been established that the use of a trained artificial neural network increases the diagnostic efficiency by 15% and reduces the time to perform diagnostic microscopy by 60.3%. The application of the proposed method allows to reduce the time of microscopic examination, improve the accuracy of interpretation of the results, automate methods for identifying causative agents of ringworm in small animals and take timely measures to treat the animal.

Logarithm-approximate floating-point multiplier is applicable to power-efficient neural network training

Artificial neural networks (ANN) have been widely used in the field of data classification. Normally, training of neural network is applied with the traditional back propagation technique. As, this approach has various drawbacks, training of neural network is done with Particle Swarm Optimization (PSO). PSO has been widely used to solve the diverse kind of optimization problems. Population initialization performs a significant role in meta-heuristic algorithms. This paper describes a new initialization population approach Log Logistic termed as PSOLL-NN to create the initialization of the swarm. The proposed algorithm has been tested for weight optimization of feed forward neural network; and compared with back propagation Algorithm (BPA), standard PSO (PSO-NN), PSO initialized with Halton Sequence (PSOH-NN), Torus sequence (PSOT-NN) and Sobol sequence (PSOS-NN). The experimental results show that the proposed technique performed exceptionally better than the other traditional techniques. Moreover, the outcome of our work presents a foresight that how the proposed initialization technique can be used as an efficient alternative to standard training approaches for the data classification problems.

The accurate of i identificationntrinsically disordered proteins or protein regions is of great importance, as they are involved in critical biological process and related to various human diseases. In this paper, we develop a deep neural network that is based on the well-known VGG16. Our deep neural network is then trained through using 1450 proteins from the dataset DIS1616 and the trained neural network is tested on the remaining 166 proteins. Our trained neural network is also tested on the blind test set R80 and MXD494 to further demonstrate the performance of our model. The MCC value of our trained deep neural network is 0.5132 on the test set DIS166, 0.5270 on the blind test set R80 and 0.4577 on the blind test set MXD494. All of these MCC values of our trained deep neural network exceed the corresponding values of existing prediction methods.

Training of neural networks (NNs) has emerged as a major consumer of both computational and energy resources. Quantum computers were coined as a root to facilitate training, but no experimental evidence has been presented so far. Here we demonstrate that quantum annealing platforms, such as D-Wave, can enable fast and efficient training of classical NNs, which are then deployable on conventional hardware. From a physics perspective, NN training can be viewed as a dynamical phase transition: the system evolves from an initial spin glass state to a highly ordered, trained state. This process involves eliminating numerous undesired minima in its energy landscape. The advantage of annealing devices is their ability to rapidly find multiple deep states. We found that this quantum training achieves superior performance scaling compared to classical backpropagation methods, with a clearly higher scaling exponent (1.01 vs. 0.78). It may be further increased up to a factor of 2 with a fully coherent quantum platform using a variant of the Grover algorithm. Furthermore, we argue that even a modestly sized annealer can be beneficial to train a deep NN by being applied sequentially to a few layers at a time.

Surface deflection is a phenomenon that causes fine wrinkles on the outer surfaces of sheet metal and deteriorates product external appearance. It is quantitatively defined as the difference between the section curve of the sheet and the ideal curve. In this study, using neural networks, a prediction model for surface deflection according to material properties was constructed and combined with a genetic algorithm; the combination of the material properties was studied to predict the minimum surface deflection. Because of the limited number of simulation data, neural networks were developed using several sampling methods such as central composite design, Latin hypercube sampling, and random sampling. In the training of the neural networks, the optimal hyper-parameter of the neural network was found automatically using Latin hypercube sampling. In conclusion, for prediction of surface deflection in rectangular embossing, neural networks made by central composite design showed the best performance. In addition, it was confirmed that the procedure of combining automatic training of a neural network and the genetic algorithm accurately predicted the set of material properties that generates the minimum surface deflection. Also, the quantity of surface deflection predicted by the neural network was very close to that predicted by finite element analysis.

Approaches presented today in the scientific literature suggest that there are no methodological solutions based on the training of artificial neural networks to predict the direction of industrial development, taking into account a set of factors—innovation, environmental friendliness, modernization and production growth. The aim of the study is to develop a predictive model of performance management of innovative industrial systems by building neural networks. The research methods were correlation analysis, training of neural networks (species—regression), extrapolation, and exponential smoothing. As a result of the research, the estimation efficiency technique of an innovative industrial system in a complex considering the criteria of technical modernization, development, innovative activity, and ecologization is developed; the prognostic neural network models allow to optimize the contribution of signs to the formation of target (set) values of indicators of efficiency for macro and micro-industrial systems that will allow to level a growth trajectory of industrial systems; the priority directions of their development are offered. The following conclusions: the efficiency of industrial systems is determined by the volume of sales of goods, innovative products and waste recycling, which allows to save resources; the results of forecasting depend significantly on the DataSet formulated. Although multilayer neural networks independently select important features, it is advisable to conduct a correlation analysis beforehand, which will provide a higher probability of building a high-quality predictive model. The novelty of the research lies in the development and testing of a unique methodology to assess the effectiveness of industrial systems: it is based on a multidimensional system approach (takes into account factors of innovation, environmental friendliness, modernization and production growth); it combines a number of methodological tools (correlation, ranking and weighting); it expands the method of effectiveness assessment in terms of the composition of variables (previously presented approaches are limited to the aspects considered).

In present paper, concrete compressive strength is evaluated using back propagation feed-forward artificial neural network. Training of neural network is performed using Levenberg-Marquardt learning algorithm for four architectures of artificial neural networks, one, three, eight and twelve nodes in a hidden layer in order to avoid the occurrence of overfitting. Training, validation and testing of neural network is conducted for 75 concrete samples with distinct w/c ratio and amount of superplasticizer of melamine type. These specimens were exposed to different number of freeze/thaw cycles and their compressive strength was determined after 7, 20 and 32 days. The obtained results indicate that neural network with one hidden layer and twelve hidden nodes gives reasonable prediction accuracy in comparison to experimental results (R=0.965, MSE=0.005). These results of the performed analysis are further confirmed by calculating the standard statistical errors: the chosen architecture of neural network shows the smallest value of mean absolute percentage error (MAPE=, variance absolute relative error (VARE) and median absolute error (MEDAE), and the highest value of variance accounted for (VAF).

Neural network training is inherently sequential where the layers finish the forward propagation in succession, followed by the calculation and back-propagation of gradients (based on a loss function) starting from the last layer. The sequential computations significantly slow down neural network training, especially the deeper ones. Prediction has been successfully used in many areas of computer architecture to speed up sequential processing. Therefore, we propose ADA-GP, which uses gradient prediction adaptively to speed up deep neural network (DNN) training while maintaining accuracy. ADA-GP works by incorporating a small neural network to predict gradients for different layers of a DNN model. ADA-GP uses a novel tensor reorganization method to make it feasible to predict a large number of gradients. ADA-GP alternates between DNN training using backpropagated gradients and DNN training using predicted gradients. ADA-GP adaptively adjusts when and for how long gradient prediction is used to strike a balance between accuracy and performance. Last but not least, we provide a detailed hardware extension in a typical DNN accelerator to realize the speed up potential from gradient prediction. Our extensive experiments with fifteen DNN models show that ADA-GP can achieve an average speed up of 1.47 × with similar or even higher accuracy than the baseline models. Moreover, it consumes, on average, 34% less energy due to reduced off-chip memory accesses compared to the baseline accelerator.

BACKGROUND: Currently, artificial intelligence in the form of artificial neural networks is being actively implemented in a number of areas of our lives, including medicine. In particular, in otorhinolaryngology, artificial neural networks are used to analyze images obtained during endoscopic examinations of patients (e.g., videolaryngoscopy) [1–3]. The interpretation of laryngoscopic images often presents significant difficulties for practicing physicians, which reduces the frequency of detection of precancerous laryngeal diseases and contributes to the increase in the number of patients with stage III–IV laryngeal cancer [4, 5]. This underscores the significance of prompt performance and accurate interpretation of the findings of endoscopic examinations of patients with laryngeal disorders. Artificial neural networks can be employed to analyze the results of videolaryngoscopy, furnishing the physician with supplementary information that can enhance diagnostic accuracy and diminish the probability of error [6, 7]. AIM: The study aims to develop and train an artificial neural network for recognizing characteristic features of laryngeal neoplasms and variants of laryngeal normality. MATERIALS AND METHODS: The study was conducted under the grant of the Moscow Center for Innovative Technologies in Healthcare (grant No. 2112-1/22) entitled “Using Neural Networks (Artificial Intelligence Algorithms) for Control and Improving the Quality of Diagnosis and Treatment of Diseases of Laryngeal and Ear Structures through Digital Technologies”.The following methods were used during the course of the study: data collection for the creation of a photobank (dataset) of medical images obtained during videolaryngoscopy; data partitioning for the formation of datasets for individual nosologies and groups of diseases; the method of consilium; analysis of the accuracy of recognition and classification of digital endoscopic images; and training of classification neural networks. Consequently, a dataset comprising 1,471 laryngeal images in digital formats (JPEG, BMP) was assembled, labelled, and uploaded for the purpose of training the artificial neural network. Of the total number of images, 410 were classified as pertaining to laryngeal formation, while 1061 were classified as variants of normality. Subsequently, the neural network was trained and tested to identify the signs of normal and laryngeal masses. RESULTS: The results of the testing of the artificial neural network indicated the formation of an inaccuracy matrix, the calculation of the value of recognition accuracy, the calculation of the quality indicators of the model performance, and the construction of the ROC curve. The developed and trained artificial neural network demonstrated an accuracy of 86% in recognizing the signs of laryngeal masses and norms. CONCLUSIONS: This study demonstrates that a trained artificial neural network can successfully distinguish between signs of normal and laryngeal masses in endoscopic photographs. With further training of the neural network and achievement of high accuracy, this technology can be used in clinical practice as an assistant in the interpretation of laryngoscopic images and early diagnosis of laryngeal masses. It can also be employed to control and improve the quality of diagnosis and treatment of diseases of the throat, nose, and ears by primary care physicians.

Training of a neural network is easier than it goes deeper. Deeper architecture makes neural networks more difficult to train because of vanishing gradient and complexity problems, and via this training, deeper neural networks become much time taking and high utilization of computer resources. Introducing residual blocks in neural networks train specifically deeper architecture networks than those used previously. Residual networks gain this achievement by attaching a trip connection to the layers of artificial neural networks. This paper is about showing residual networks and how they work like formulas, we will see residual networks obtain good accuracy, and as well as the model is easier to optimize because Res Net makes training of large structured neural networks more efficient. We will check residual nets on the Image Net dataset with a depth of 152 layers which is 8x more intense than VGG nets yet very less complex. After building this architecture of residual nets gets error up to 3.57% on the Image Net test dataset. We also compare the Res Net result to its equivalent Convolutional Network without residual connection. Our results show that ResNet provides higher accuracy but apart from that, it is more prone to over fitting. Stochastic augmentation of training datasets and adding dropout layers in networks are some of the over fitting prevention methods.

The present paper covers the issue of training of an artificial neural network in an intelligent diagnostic system whose purpose is to evaluate repairable technical objects. The structure of the diagnostic system was characterized, and the measurement and diagnostic subsystems were described. An artificial neural network is an important element in an intelligent diagnostic subsystem. The structure, the algorithm, the organization of a neural network and the basic relations that describe its work were presented. The information presented in the form of the vectors of diagnostic signals, and their standard vectors constitute the primary information base used in “DIAG” computer program. Training of an artificial neural network is an important aspect that is presented in the paper. The issue concerning these problems is not presented in the literature. Training of a network was presented on the grounds of teaching vectors, which are determined in a diagnostic system in the process of a simulation of a specific state in the object examined. An example of training of a network was presented in a diagnostic system which evaluates a control system of the operation of a car engine. Appropriate connections were presented for the purpose of a qualitative assessment of the training process of a neural network.

In a bid to predict the propagation loss of electromagnetic signals, different models based on empirical and deterministic formulas have been used. In this study, different artificial neural network models which are very effective for prediction were used for the prediction of signal power loss in a microcell environment, Obio-Akpor, Port Harcourt, Nigeria. The signal power loss of the area is studied based on three artificial neural network algorithms with nine training functions. For the training of the artificial neural network, the input data were the distance from the transmitter and the signal power loss. Training of neural network is a demanding task in the field of supervised learning research. This is because the main difficulty in adopting artificial neural network is in finding the most suitable combination of learning and training functions for the prediction task. We compared the performance of three training algorithms in feedforward back propagation multi layer perceptron neural network. Nine training functions under three training algorithms were selected: the Gradient descent based algorithms, the Conjugate gradient based algorithms and the Quasi-Newton based algorithms. The work compared the training algorithms on the basis of mean square error, mean absolute error, standard deviation, correlation coefficient, regression on training and validation and the rate of convergence. The general performance of the training functions demonstrates their effectiveness to yield accurate results in short time. The conclusion on the training functions is based on the simulation results using measurement data from the micro environment.

Comparing performances of backpropagation and genetic algorithms in the data classification

Scientific machine learning is a burgeoning discipline which blends scientific computing and machine learning.Traditionally, scientific computing focuses on large-scale mechanistic models, usually differential equations, that are derived from scientific laws that simplified and explained phenomena.On the other hand, machine learning focuses on developing non-mechanistic data-driven models which require minimal knowledge and prior assumptions.The two sides have their pros and cons: differential equation models are great at extrapolating, the terms are explainable, and they can be fit with small data and few parameters.Machine learning models on the other hand require "big data" and lots of parameters but are not biased by the scientists ability to correctly identify valid laws and assumptions.However, the recent trend has been to merge the two disciplines, allowing explainable models that are data-driven, require less data than traditional machine learning, and utilize the knowledge encapsulated in centuries of scientific literature.The promise is to fuse a priori domain knowledge which doesn't fit into a "dataset", allow this knowledge to specify a general structure that prevents overfitting, reduces the number of parameters, and promotes extrapolatability, while still utilizing machine learning techniques to learn specific unknown terms in the model.This has started to be used for outcomes like automated hypothesis generation and accelerated scientific simulation.The purpose of this blog post is to introduce the reader to the tools of scientific machine learning, identify how they come together, and showcase the existing open source tools which can help one get started.We will be focusing on differentiable programming frameworks in the major languages for scientific machine learning: C++, Fortran, Julia, MATLAB, Python, and R.We will be comparing two important aspects: efficiency and composability.Efficiency will be taken in the context of scientific machine learning: by now most tools are well-optimized for the giant neural networks found in traditional machine learning, but, as will be discussed here, that does not necessarily make them efficient when deployed inside of differential equation solvers or when mixed with probabilistic programming tools.Additionally, composability is a key aspect of scientific machine learning since our toolkit is not ML in isolation.Our goal is not to do machine learning as seen in a machine learning conference (classification, NLP, etc.), and it's not to do traditional machine learning as applied to scientific data.Instead, we are putting ML models and techniques into the heart of scientific simulation tools to accelerate and enhance them.Our neural networks need to fully integrate with tools that simulate satellites and robotics simulators.They need to integrate with the packages that we use in our scientific work for verifying numerical accuracy, tracking units, estimating uncertainty, and much more.We need our neural networks to play nicely with existing packages for delay