Neural Network Model In Order Research Articles

Device packaging and integration optimization based on neural network method: Effect of microchannel structure on heat sink performance

With the continuous improvement of power density of electronic device packaging, microchannel structure based on advanced packaging integration technology has become one of the hot topics in thermal management research. In this paper, two heat sink models are numerically simulated: rectangular microchannel and microchannel-pinfin hybrid. The effects of microchannel structure, inlet/outlet arrangement and pinfin design on heat sink performance were studied. The heat sink performance is evaluated by the maximum temperature at hotspot area, the total thermal resistance and the mean absolute temperature deviation (MATD). The research results show that at the same Reynolds number, the hybrid structure with pinfin has better heat sink performance. Compared with the rectangular microchannel structure, the maximum temperature at the hotspot area is reduced by 20.45 K and the total thermal resistance is reduced by 5.9 %. Compared with the three inlet/outlet arrangements of I-type, U-type and Z-type, the I-type hybrid structure has the best heat sink performance. The average absolute temperature deviation is about 10 K at high Reynolds number, and the temperature distribution is more uniform. In addition, the heat sink performance is improved by increasing the pinfin size to expand the heat transfer area. This paper uses simulation results to build an artificial neural network model in order to quickly and accurately predict numerical results under different parameter conditions. The network model has good accuracy and robustness, with a mean absolute error of 0.376 %. The prediction model takes heat source power, Reynolds number and pinfin structural parameters as input. Rapidly predicting the performance of heat sinks with different structural parameters under different boundary conditions will effectively save experimental and computational costs. This research provides new ideas and tools for optimizing heat sink design, and helps improve the application of microchannel cooling technology in electronic devices.

Investigation of interfacial debonding identification for concrete filled steel tube columns based on acoustic signals

A method was proposed to address challenges in detecting debonding in concrete-filled steel tube (CFST) columns by analyzing acoustic signals. In this approach, wavelet time-frequency diagrams are initially derived through the application of continuous wavelet transform to acoustic signals acquired from tapping on columns. The wavelet time-frequency diagrams serve as data samples for training the MobileNetv2 convolutional neural network model in order to identify concrete debonding. The laboratory test results show that the MobileNetv2 model achieved 100 % accuracy for both the training and verification sets, and 99 % accuracy for the test set. Four CFST columns in a high-rise building under construction were also measured. The findings indicate a recognition accuracy ranging from 86.7 % to 90 % and an error rate that falls within an acceptable range. These results confirm the viability of the method outlined in this paper for detecting debonding in CFST columns using acoustic signals.

Neural network model development for detecting atypical mitoses in histological slides

Background: Modern computer systems allow digitizing and examining images of histological preparations, which led the authors to the idea of using machine learning tools in digital pathohistology. The ability of neural networks to find sub-visual image features in digitized histological preparations provides the basis for better qualitative and quantitative image analysis. Existing machine learning methods provide good accuracy and speed in recognizing various images, which gives hope for their wide application, including in oncologic diagnostics. AIM: Use methods of mathematical modeling to identify pathological mitoses in histological preparations as the main sign of the difference between malignant and benign tumor growth. MATERIALS AND METHODS: Histological images of the N.N. Priorov National Medical Research Center of Traumatology and Orthopedics were used as a data set for the neural network model. The model was tested using 188 histologic slides from 67 patients treated at the institute. Histological preparations were scanned on a Leica Aperio CS2 microscope with a ×400 resolution and converted into JPEG format with further processing. Next, the test images were analyzed in streaming mode using the created neural network model in order to obtain the coordinates of the desired diagnostic object — pathological mitosis and the probability with which the model found the object of this category. The obtained images were analyzed by a pathologist to determine whether the detected object corresponded to pathological mitosis. RESULTS: The authors have chosen an architecture, developed a methodology for training a neural network, and created a model that can be used to detect pathologic mitoses in histologic preparations. The authors do not attempt to replace the physician, but show the possibility of an integrated approach to data analysis by a computer system and a pathologist. Conclusions: The developed mathematical model of neural network used as a part of technological solution for recognizing pathological mitoses in scanned histological preparations can be used as a tool to reduce the time of research and increase the accuracy of diagnosis by a pathologist.

COMPARATIVE ANALYSIS OF NEURAL NETWORK MODELS FOR THE PROBLEM OF SPEAKER RECOGNITION

The subject matter of the article are the neural network models designed or adapted for the problem of voice analysis in the context of the speaker identification and verification tasks. The goal of this work is to perform a comparative analysis of relevant neural network models in order to determine the model(s) that best meet the chosen formulated criteria, – model type, programming language of model’s implementation, parallelizing potential, binary or multiclass, accuracy and computing complexity. Some of these criteria were chosen because of universal importance, regardless of particular application, such as accuracy and computational complexity. Others were chosen due to the architecture and challenges of the scientific communication system mentioned in the work that performs tasks of the speaker identification and verification. The relevance of the paper lies in the prevalence of audio as a communication medium, which results in a wide range of practical applications of audio intelligence in various fields of human activity (business, law, military), as well as in the necessity of enabling and encouraging efficient environment for inward-facing audio-based scientific communication among young scientists in order for them to accelerate their research and to acquire scientific communication skills. To achieve the goal, the following tasks were solved: criteria for models to be judged upon were formulated based on the needs and challenges of the proposed model; the models, designed for the problems of speaker identification and verification, according to formulated criteria were reviewed with the results compiled into a comprehensive table; optimal models were determined in accordance with the formulated criteria. The following neural network based models have been reviewed: SincNet, VGGVox, Jasper, TitaNet, SpeakerNet, ECAPA_TDNN. Conclusions. For the future research and practical solution of the problem of speaker authentication it will be reasonable to use a convolutional neural network implemented in the Python programming language, as it offers a wide variety of development tools and libraries to utilize.

Fast Image Classification Algorithms Based on Sequential Analysis

This work is devoted to fast image recognition techniques based on statistical sequential analysis. We examine the possibility to sequentially process principal components and organize a convolutional neural network with early exits. We pay special attention to sequentially learning a multi-task lightweight neural network model in order to predict several facial attributes (age, gender, and ethnicity) based on preliminary training on the face classification task. We specifically note that the entire above-mentioned model should be fine-tuned in order to deal with the emotion recognition problem. Experimental evaluation on several datasets demonstrates that the proposed approach has high accuracy and very low running time and space complexity compared to known state-of-the-art methods.

Statistical Deep Learning for Spatial and Spatiotemporal Data

Deep neural network models have become ubiquitous in recent years and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., images) and time (e.g., sequences). Indeed, deep models have also been extensively used by the statistical community to model spatial and spatiotemporal data through, for example, the use of multilevel Bayesian hierarchical models and deep Gaussian processes. In this review, we first present an overview of traditional statistical and machine learning perspectives for modeling spatial and spatiotemporal data, and then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications. These hybrid models integrate statistical modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm. We conclude by giving an overview of computational technologies that have proven useful for these hybrid models, and with a brief discussion on future research directions.

Bitcoin monthly return forecast: A comparison of ARIMA and multi layer Perceptron Artificial Neural Network

In this paper, we compare the predictive power of Auto Regressive Integrated Moving Averages (ARIMA) and Multi-Layer Perceptron Artificial Neural Networks (MLP ANN) model to short-term forecast the monthly returns of Bitcoin cryptocurrency. We evaluate the performance of two models using time series with monthly data from January 2018 to December 2021. The key parameters for the final assessment of prognostic models are the values of Root Mean Square Error-RMSE and Forecast Error-FE. The results of the short-term BTC return forecast showed better properties of composite compared to univariate time series forecasting models, i.e., higher prognostic power of the MLP ANN model compared to the selected ARIMA (1,1,3) model (lower RMSE and FE). The results point to further comparative research of prognostic models and the possibility of forming more complex and hybrid structures of neural network models in order to predict economic phenomena as accurately as possible.

중학생의 수학 교과역량에 영향을 미치는 언어 관련 개인, 가정, 수업 변인의 영향력 탐색

Objectives In this study, the influence of various predictive factors was analyzed through artificial neural network model in order to explore the influence of language-related personal, family, and class variables on middle school students' mathematics competency. Structural relationships were analyzed by constructing a model with the selected major variables as predictors and mediator, and the mathematics competency as dependent variables. Methods For this purpose, the self-report questionnaire data of 2,235 middle school students in the 3rd year(2019) of the D-educational longitudinal study and the scores of the Korean language and mathematics competency tests were used for the analysis. An artificial neural network model was constructed that predicts mathematics competency based on language-related individual, family, and class variables that can predict mathematics subject competency and Korean competency. Structural equation model analysis was conducted by setting the structural relationship between language-related variables and mathematics competency as a research model. Results The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. The purposes of this study were to specify variables related to convergence competency, and to analyze the structural relationships among them. Conclusions As a result of checking the correlation coefficient between the training data and the predicted value in the process of building the artificial neural network model, the average of the error was 0.7, and the correlation coefficient was . 68, indicating adequate prediction performance. As a result of confirming the structural relationship between language-related variables and Korean competency and mathematics competency, individual, family, and class variables directly or indirectly had a significant positive effect on mathematics subject competency.

Semi-empirical dynamics modeling of a bicycle robot based on feature selection and RHONN

Aiming at the difference between a bicycle robot dynamics model based on prior knowledge and a real robot, the robot modeling problem is studied. A set of features is extracted from the dynamics model of a bicycle robot. The trigonometric functions in the features are replaced with a linear combination of several sigmoid functions to form a new set of features. An index describing the importance of features is designed based on the maximal information coefficient (MIC). The features are ranked using the feature importance index. The index of system identification fitness (FIT) is regarded as the likelihood function term in Bayesian information criterion (BIC). The penalty term of BIC is constructed with the number of both the features and weight parameters in the recurrent high order neural network (RHONN) model. A semi-empirical dynamics modeling method based on feature selection is proposed. An inverse optimal controller is designed to stabilize the bicycle robot. The effectiveness of the modeling method is verified through simulation and real robot experiments.

A System for Sustainable Usage of Computing Resources Leveraging Deep Learning Predictions

In this paper, we present the benefit of using deep learning time-series analysis techniques in order to reduce computing resource usage, with the final goal of having greener and more sustainable data centers. Modern enterprises and agile ways-of-working have led to a complete revolution of the way that software engineers develop and deploy software, with the proliferation of container-based technology, such as Kubernetes and Docker. Modern systems tend to use up a large amount of resources, even when idle, and intelligent scaling is one of the methods that could be used to prevent waste. We have developed a system for predicting and influencing computer resource usage based on historical data of real production software systems at CERN, allowing us to scale down the number of machines or containers running a certain service during periods that have been identified as idle. The system leverages recurring neural network models in order to accurately predict the future usage of a software system given its past activity. Using the data obtained from conducting several experiments with the forecasted data, we present the potential reductions on the carbon footprint of these computing services, from the perspective of CPU usage. The results show significant improvements to the computing power usage of the service (60% to 80%) as opposed to just keeping machines running or using simple heuristics that do not look too far into the past.

Target Detection and Network Optimization: Deep Learning in Face Expression Feature Recognition

Facial expression recognition technology has been more and more in demand in security, entertainment, education, medical, and other domains as artificial intelligence has advanced, and face expression recognition technology based on deep learning has become one of the research hotspots. However, there are still some issues with the existing deep learning convolutional neural network; the feature extraction technique has to be improved, and the design of the detailed network structure needs to be optimized. It is critical to do more research on the deep learning convolutional neural network model in order to increase the accuracy of face facial expression detection. In this paper, a deep learning convolutional neural network structure combining VGG16 convolutional neural network and long and short-term memory networks is designed to address the shortcomings of existing deep learning methods in face expression recognition, which are prone to overfitting and gradient disappearance, resulting in low test accuracy. This structure easily and effectively collects facial expression information and then classifies the retrieved features using a support vector machine to detect face expressions. Finally, the fer2013 dataset is used to train face expression recognition, and the results demonstrate that the built deep convolutional neural network model can effectively increase face expression identification accuracy.

Customized deep neural network model for autonomous and efficient surveillance of wildlife in national parks

As National parks are one of the best places of attraction to the tourists, the so-called abode of the animals has to be protected due to the frequent intervention of human beings. On the other hand, the lives of human beings have also to be insulated from the sudden and vulnerable attack of the animals. When it comes to ‘life’, it becomes a vital factor for humans and animals. Therefore, here arises a dire need to create a system which can protect the life and safety of human beings as well as animals. However, when it comes to mutual safety of both, there has not been an effective solution till date that can practically be implemented in a national park or a sanctuary. In this scenario, ‘Video Object Identifying’ using Machine Learning Technique has been proven to render promising results in terms of accuracy and performance. Classifying and identifying objects using Customized Convolutional Neural-Network Model in order to detect the multi-class objects (vehicle and animal) become the aim of this present research. In this research, five categories of data sets namely lion, elephant, tiger, car and motorcycle have been used for training and testing. The custom model for video object detection renders with an accuracy of 82% which proves its efficiency in detecting the multi class datasets.

PREDICTION OF RESIDUAL RESISTANCE OF A TRIMARAN VESSEL BY USING AN ARTIFICIAL NEURAL NETWORK

Trimaran hull forms have been popular recently in both commercial and military usage due to reduction in resistance at high speeds, better stability, and greater deck area compared to conventional monohull vessels. Determination of the location of the side hulls is most critical to get higher hydrodynamic performance. Therefore, many studies in the literature are related to defining the location of the side hulls for trimaran vessels. Most of the studies have been carried out experimentally or numerically. In this study, an artificial neural network (ANN) model was used to predict the residual resistance coefficient of a trimaran model. The model uses four parameters which are the transverse and longitudinal positions of the side hulls, the longitudinal centre of buoyancy and the Froude number to predict the residual resistance of the trimaran model. The experimental data of the trimaran model were used to train the neural network model in order to develop a more reliable model. Several neural network models were developed and tested to find the one with minimum error. The study showed that the residual resistance coefficients of the trimaran model were predicted with high accuracy levels compared to the model experimental data. It was also shown that an ANN is a useful alternative method to model tests and numerical simulations. The developed model can be used to reduce the number of model tests or numerical simulations as well as to obtain the optimum location of the side hulls in terms of resistance.

Application of BP Neural Network in Classification and Prediction of Blended Learning Achievements

Analyzing and predicting the learning behavior data of students in blended teaching can provide reference basis for teaching. Aiming at weak generalization ability of existing algorithm models in performance prediction, a BP neural network is introduced to classify and predict the grades of students in the blended teaching. L2 regularization term is added to construct the BP neural network model in order to reduce the risk of overfitting. Combined with Pearson coefficient, effective feature data are selected as the validation dataset of the model by mining the data of Chao-Xing platform. The performance of common machine learning algorithms and the BP neural network are compared on the dataset. Experiments show that BP neural network model has stronger generalizability than common machine learning models. The BP neural network with L2 regularization has better fitting ability than the original BP neural network model. It achieves better performance with improved accuracy.

Comparison of Deep Neural Network Models and Effectiveness of EMG Signal Feature Value for Estimating Dorsiflexion

Robotic ankle–foot orthoses (AFO) are often used for gait rehabilitation. Our research focuses on the design and development of a robotic AFO with minimum number of sensor inputs. However, this leads to degradation of gait estimation accuracy. To prevent degradation of accuracy, we compared a few neural network models in order to determine the best network when only two input channels are being used. Further, the EMG signal feature value of average rate of change was used as input. LSTM showed the highest accuracy. However, MLP with a small number of hidden layers showed results similar to LSTM. Moreover, the accuracy for all models, with the exception of LSTM for one subject (SD), increased with the addition of feature value (average rate of change) as input. In conclusion, time-series networks work best with a small number of sensor inputs. However, depending on the optimizer being used, even a simple network can outrun a deep learning network. Furthermore, our results show that applying EMG signal feature value as an input tends to increase the estimation accuracy of the network.

An application of a feed-forward neural network model for wind speed predictions

ABSTRACT Since wind energy covers an important part of the increasing electricity demands, accurate wind speed and direction forecasts are essential for the optimal operation of both the wind farms and the grid. Here we present the set-up and optimisation of a selected feed-forward neural network model in order to forecast, 24-hour ahead, the wind speed at specific points of three wind farms, using past data measured at the same locations. The model combines the k-fold cross-validation method for selecting the appropriate input data consisted either of hourly or 10-min average input values, while the impact of the length of the input time series is also investigated. Our method is tested against persistence method forecasts using the RMSE and MAPE statistics.

Improved damage detection in Pelton turbines using optimized condition indicators and data-driven techniques

The health condition of hydraulic turbines is one of the most critical factors for the operation safety and financial benefits of a hydro power plant. After the massive entrance of intermittent renewable energies, hydropower units have to regulate their output much more frequently for the balancing of the power grid. Under these conditions, the components of the machine have to withstand harsher excitation forces, which are more likely to produce damage and eventual failure in the turbines. To ensure the reliability of these machines, improved condition monitoring techniques are increasingly demanded. In this article, the feasibility of upgrading condition monitoring of Pelton turbines using novel vibration indicators and data-driven techniques is discussed. The new indicators are selected after performing a detailed analysis of the dynamic behavior of the turbine using numerical models and field measurements. After that, factor analysis is carried out in order to assess which are the most informative indicators and to reduce the dimension of the input data. For the validation of the proposed method, monitoring data from an actual Pelton turbine that suffered from an important fatigue failure due to a crack propagation on the buckets have been used. The novel condition indicators as well as classical indicators based on the spectrum and harmonics levels have been obtained while the machine was in good operation, during different stages of damage and after repair. All of these have been used to train an artificial neural network model in order to predict the evolution of the crack until failure occurs. The results show that using the improved monitoring methodology enhances the ability to predict the appearance of damage in comparison to typical condition indicators.

Convolutional neural network-based models for diagnosis of breast cancer.

Breast cancer is the most prevailing cancer in the world and each year affecting millions of women. It is also the cause of largest number of deaths in women dying in cancers. During the last few years, researchers are proposing different convolutional neural network models in order to facilitate diagnostic process of breast cancer. Convolutional neural networks are showing promising results to classify cancers using image datasets. There is still a lack of standard models which can claim the best model because of unavailability of large datasets that can be used for models’ training and validation. Hence, researchers are now focusing on leveraging the transfer learning approach using pre-trained models as feature extractors that are trained over millions of different images. With this motivation, this paper considers eight different fine-tuned pre-trained models to observe how these models classify breast cancers applying on ultrasound images. We also propose a shallow custom convolutional neural network that outperforms the pre-trained models with respect to different performance metrics. The proposed model shows 100% accuracy and achieves 1.0 AUC score, whereas the best pre-trained model shows 92% accuracy and 0.972 AUC score. In order to avoid biasness, the model is trained using the fivefold cross validation technique. Moreover, the model is faster in training than the pre-trained models and requires a small number of trainable parameters. The Grad-CAM heat map visualization technique also shows how perfectly the proposed model extracts important features to classify breast cancers.

Prediction of the length of service at the onset of coal workers’ pneumoconiosis based on neural network

Three environmental parameters, i.e. dust concentrations, dust dispersion, and free silica content, were introduced into the traditional indices of the neural network model in order to construct a new prediction index and explore a new method for preventing the incidence of pneumoconiosis with intelligent accuracy and universality. Data of the pneumoconiosis patients from Huabei Mining Group (HBMG) of China from 1980 to 2017 were collected. SPSS22.0 was used to develop the combined models based on Back Propagation (BP) neural network model, Radial Basis Function (RBF) neural network model, and Multiple Linear Regression (MLR) model. The paired sample t-test was performed between the real and predicted values. According to this model, it was predicted that 382 coal workers in HBMG were likely to suffer from pneumoconiosis in 2022 and the incidence rate was 4.48%. It is necessary to take prevention measures and transfer these workers from their current positions. In four combined models, the BP-MLR combined model achieved the optimal error parameters and the most accurate prediction. This study provided a scientific basis for effective control and prevention of the incidence of the pneumoconiosis.

An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity.

A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.