Deep Belief Network Algorithm Research Articles

Novel computer aided diagnostic system using hybrid neural network for early detection of pancreatic cancer

One of the most dangerous tumours in the world, pancreatic cancer (PC), has an unimpressive five-year survival rate of about 5%. An early PC identification is crucial for raising patient survival rates. Diagnosis of PC requires computed tomography (CT), magnetic resonance imaging (MRI) with magnetic resonance cholangiopancreatography (MRCP), or biopsy. The proposed CAD design approach includes image preprocessing, segmentation, feature extraction, and classification phases. Preprocessing is done by using Colour conversion and an isotropic diffusion filter approaches. After that, proposed Fuzzy K-NN Equality algorithm used in segmentation procedures. Deep Learning with feature extraction is used as a classification tool. Tumour cells are classified using the features collected from the pancreatic sample. Train values and testing datasets are part of the image classification criterion. For the purpose of detecting pancreatic cancer, a hybrid Deep Convolutional Neural Network with Deep Belief Network (DCNN_DBN) algorithm is used. According to the experimental findings, the current CAD system offers massive prospects as well as safety in the automated diagnosis of both benign as well as malignant cancers and produces the accuracy of 99.6%. Using this classifier, computing complexity is massively diminished. The suggested technique could be enhanced to detect more pancreatic cancer cell abnormalities.

Multi‐disease classification model using deep neural network and Strassen's rectilinear fine‐tune bouncing training algorithm

AbstractA deep neural network (DNN) is being used in the healthcare industry to improve care delivery at a lower cost and in less time. A DNN is well‐known for its diagnostic applications. However, it is also increasingly being utilized to guide healthcare management decisions. DNNs have been applied to real‐life disease classification problems. The performances of DNN are improved by reducing the training time, performing fast classification, and improving the parameters such as accuracy and error rate, and so forth. We have proposed a hybrid DNN named Strassen's rectilinear fine‐tune bouncing training (SRFBT) Algorithm by combining Strassen's theorem and bouncing training algorithm. The simulation result shows that the SRFBT algorithm outperforms both the DNN, support vector machine, radial basis function network and deep belief network algorithms in terms of network training, testing time and accuracy on various UCI machine learning healthcare datasets. The SRFBT has improved performance with a minimum average training time of 43.6249 and a maximum accuracy of 96.86.

Temperature modeling of wave rotor refrigeration process based on elastic net variable selection and deep belief network

Optimal operation modeling plays an important role in wave rotor refrigeration process; however, considering covariance among multiple variables and high nonlinearity in wave rotor refrigeration process, it becomes more and more difficult to establish an accurate operation modeling using first-principles methods. This study proposed a novel modeling algorithm for the temperature parameter of the wave rotor refrigeration process based on elastic net and dingo optimization deep belief network (Enet-DOA-DBN). Firstly, to determine the correlation between the input variables and reduce the dimension of the input variables, the elastic net (Enet) algorithm is used to select the input variables that are irrelevant to the temperature parameter of the wave rotor refrigeration process. In this way, the covariance between multiple variables is eliminated and the model structure is simplified. Secondly, in order to improve the generalization of the temperature parameter model, a deep belief network (DBN) deep learning is proposed for modeling the temperature parameter of the wave rotor refrigeration process. Considering that the numerous hyperparameters of DBN algorithm have a great impact on the training and prediction results, the hyperparameters are optimized by the dingo optimization algorithm (DOA). The proposed Enet-DOA-DBN algorithm is validated by simulation using the benchmark data sets and the wave rotor refrigeration industrial process data sets. The simulation results show that the proposed Enet-DOA-DBN algorithm has good generalization ability, meanwhile it can effectively implement variable selection and simplify the model structure.

DDoS ATTACK DETECTION METHODS BASED ON DEEP LEARNING IN HEALTHCARE

Software-defined network (SDN) is a new network structure, which has the characteristics of centralized management and programmable, and is widely used in the field of Internet of things. Distributed denial of service (DDoS) attack is one of the most threatening attacks in SDN network. How to effectively detect DDoS attacks has become a research hotspot in the field of SDN security management. Aiming at the above problems, this paper proposes a DDoS attack detection method based on Deep belief network (DBN) in SDN network architecture. By extracting the characteristics of OpenFlow switch flow table entries, DBN algorithm is trained to detect whether there are DDoS attacks. The experimental results show that the method is better than the other algorithms in accuracy, precision and recall.

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

Vehicle emissions seriously affect the air environment and public health. The dynamic estimation method of vehicle emissions changing over time on the road network has always been the bottleneck of air quality simulation. The dynamic traffic volume is one of the important parameters to estimate vehicle emission, which is difficult to obtain effectively. A novel estimation method of whole sample traffic volumes and emissions on the entire road network based on multifactor Macroscopic Fundamental Diagram (MFD) is proposed in this paper. First, the intelligent clustering and recognition methods of traffic flow patterns are constructed based on neural network and deep-learning algorithms. Then, multifactor MFD models are developed considering different road types, traffic flow patterns and weekday peak hours. Finally, the high spatiotemporal resolution estimation method of whole sample traffic volumes and emissions are constructed based on MFD models. The results show that traffic flow patterns are clustered efficiently by the Self-Organizing Maps (SOM) algorithm combined with the direct time-varying speed index, which describe 91.7% traffic flow states of urban roads. The Deep Belief Network (DBN) algorithm precisely recognizes 92.1% of the traffic patterns based on the speeds of peak hours. Multifactor MFD models estimate the whole sample traffic volumes with a high accuracy of 91.6%. The case study shows that the vehicle emissions are evaluated dynamically based on the novel estimation method proposed in this paper, which is conducive to the coordinated treatment of air pollution.

A deep belief network-based clinical decision system for patients with osteosarcoma.

Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making.

Research on the Deep Learning Technology in the Hull Form Optimization Problem

A high−accuracy objective function evaluation method is crucial in ship hull form optimization. This study proposes a novel approximate ship hull form optimization framework using the deep learning technology, deep belief network algorithm. To illustrate the advantages of using the deep belief network algorithm in the prediction of total resistance, two traditional surrogate models (ELMAN and RBF neural networks) are also employed in this study to predict total resistance for different modified ship models. It can be seen from the results that the deep belief network algorithm is more suitable for forecasting total resistance of a DTMB5512 ship model than the traditional surrogate models. Following this, two design variables are selected to alter the bow geometry of the DTMB5512 ship model. The total resistance for different modified ship hulls is estimated using the deep belief network algorithm. Furthermore, an optimal solution with minimum total resistance in a two−dimensional space is obtained using the particle swarm optimization algorithm. The optimization results indicate that the optimization framework using the deep belief network algorithm can obtain an optimal solution with the smallest total resistance for different ship speeds.

Theater Music Data Acquisition and Genre Recognition Using Edge Computing and Deep Brief Network

Artificial intelligence (AI) and the Internet of Things (IoT) make it urgent to push the frontier of AI to the network edge and release the potential of edge big data. The model’s accuracy in data acquisition and music genre classification (MGC) is further improved based on theater music data acquisition. First, machine learning and AI algorithms are used to collect data on various devices and automatically identify music genres. The data collected by edge devices are safe and private, which shortens the time delay of data processing and response. In addition, the deep belief network (DBN)-based MGC algorithm has better overall recognition and classification effect on music genres. The MGC accuracy of the proposed improved DBN algorithm is nearly 80%, compared to 30%–40% of the traditional algorithms. The DBN algorithm is more accurate than the traditional classical algorithm in MGC. The research has an important reference value for developing Internet technology and establishing a music recognition model.

Microblog Emotion Analysis Method Using Deep Learning in Spark Big Data Environment

Aiming at the problem that the existing methods in the big data environment cannot extract the emotional features of microblog sufficiently and the average accuracy of analysis results is low, a microblog emotion analysis method using deep learning in spark big data environment is proposed. First, the Jieba word segmentation method is used to process text comments, so as to reduce the interference of irregular grammar and nonstandard words on the emotion analysis task of microblog text. Then, features based on affective rules, unary word features, syntactic features, and dependent word collocation features are selected. In order to prevent the dimension disaster caused by excessive feature dimensions, the feature selection method of information gain is used to reduce the dimension of features. Finally, a microblog emotion analysis method based on deep belief network (DBN) is established, and the DBN is parallelized through spark cluster to shorten the training time. Experiments show that when the feature set is composed of TOP2000 features, the classification accuracy of the fusion of four features is 90.94%, which is higher than that of the comparison method. In addition, the training time of DBN algorithm parallelized by spark cluster is only 27.78% of that of single machine. Therefore, compared with the comparison method, the proposed method can significantly improve the performance of the microblog emotion analysis system.

An Empirical Study on the Training Characteristics of Weekly Load of Calisthenics Teaching and Training Based on Deep Learning Algorithm

Load training is an important part of the daily training of aerobics athletes. Therefore, the research on the characteristics of aerobics movement of athletes in load training has been paid extensive attention by teaching units. Deep learning algorithm in artificial intelligence algorithm is used to study the characteristics of calisthenics teaching and training, which is a beneficial exploration to improve the scientific of calisthenics training programs. The basic principles of the neural network algorithm and implementation process are described. After the basic structure and characteristics of the network are analyzed, a comprehensive solution to optimize and improve the regularized deep belief network algorithm is proposed. The results show that compared with other learning algorithms, the feature classification error rate of the optimized regularized deep belief network algorithm is 6% lower than that of other algorithms. Although the training speed of the model decreases, the convergence period of the deep neural network algorithm can better achieve the extraction accuracy of abstract features of training data when the deep learning period increases. Being less affected by input parameters, the algorithm will have better stability and be more conducive to extracting the features favorable to classification. In the field of deep learning, the advantages of the deep neural networks can effectively classify the training characteristics of aerobics athletes and provide scientific decision-making basis for subsequent teaching and training.

Safety prediction of shield tunnel construction using deep belief network and whale optimization algorithm

Due to ground loss and shallowly buried tunnels, there are serious safety problems in shield tunnel construction. To comprehensively describe the safety of shield tunnel construction, two safety control indices (ground settlement and segment floating) were applied to represent the two main aspects of construction safety (surrounding environment and tunnel structure). Here, a deep-learning method involving a deep belief network (DBN) optimized by a whale optimization algorithm (WOA) called WO-DBN is proposed to predict ground settlement and segment floating. Based on 370,404 engineering data of shield tunnel construction for Guangzhou subway Line 18 in China, the mean absolute errors of the WO-DBN method for the two indices were only 2.255 and 0.954, respectively. The results show that the WO-DBN achieves a high prediction accuracy, and that it can be effectively used for safety prediction of real shield tunnel construction. The improvement of the WO-DBN, such as through using the newly developed activation functions, should be a future research direction.

Research Based on the Application and Exploration of Artificial Intelligence in the Field of Traditional Music

Traditional Chinese music has undergone trials and tribulations. To date, traditional music has been gradually improved, preserved, and passed down, both in terms of theoretical works and traditional music varieties. However, the current state of traditional music is still a cause for concern. Whether it is scholars engaged in the study of traditional music, universities, or local government agencies, there are still areas that need to be improved individually. The need to cultivate a future audience for traditional music and to make full use of new media such as the Internet is a priority at this stage. The emergence of any new technology is bound to have some impact on the existing social system, and the emergence of artificial intelligence is no exception. Due to the limitation of technology, only a small number of basic applications have been developed, and it is the future mission of research workers whether they can develop more advanced products for music lovers to experience on the basis of ensuring the basic maturity of these applications. In this paper, we introduce the convolutional deep belief network model based on the restricted Boltzmann machine and apply the convolutional deep belief network algorithm to the music melody recognition. Firstly, it was pretrained by an unsupervised greedy layer-by-layer algorithm. Then, the network parameters were fine-tuned by a supervised network training method, and the recognition ability of the model was improved by adjusting the network parameters. The experimental results show that the recognition effect of the system is more obvious under the condition that the length of music samples is 3 s, and the recognition effect is better when the number of model layers is 2 than that when the number of layers is 1.

Fault Detection of Single Cell Battery Inconsistency in Electric Vehicle Based on Fireworks Algorithm Optimized Deep Belief Network

Abstract Because the fault characteristics of inconsistent fault single battery are not obvious in the electric vehicle battery pack, it is difficult to identify the inconsistent fault. Therefore, this paper proposes an inconsistent fault detection method based on a fireworks algorithm (FWA) optimized deep belief network (DBN). The method feeds the raw data signal into a deep belief network algorithm for training, which automatically performs feature extraction and intelligent diagnosis of inconsistencies, without requiring the time domain signal to be periodic. The top-level algorithm of the deep belief network adopts error Back Propagation (BP). Using FWA training to optimize DBN-BP, the best DBN-BP-FWA model structure can be obtained. Experimental verification was carried out using real vehicle data from electric vehicles. The inconsistency diagnosis results show that, compared with the traditional inconsistency diagnosis method, the application of this paper's method for electric vehicle single battery fault detection can obtain higher accuracy, with an average accuracy of 96.19%.

Optimal Prediction of Bitcoin Prices Based on Deep Belief Network and Lion Algorithm with Adaptive Price Size

This paper introduces a new bitcoin predictin model that includes three major phases: data collection, Feature Extraction and Prediction. The initial phase is data collection, where Bitcoin raw data are collected, from which the features are extracted in the Features Extraction phase. The feature extraction is a noteworthy mechanism for detecting the bitcoin prices on day-by-day and minute-by –minute. Such that the indexed data collected are computed regarding certain standard indicators like Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Index (RSI) and Rate of Change (ROC). These technical indicators based features are subjected to prediction phase. As the major contribution, the prediction process is made precisely by deploying an improved DBN model, whose weights and activation function are fine-tuned using a new modified Lion Algorithm referred as Lion Algorithm with Adaptive Price Size (LAAPS). Finally, the performance of proposed work is compared and proved its superiority over other conventional models.

A soft-sensing method for product quality monitoring based on particle swarm optimization deep belief networks

A novel soft-sensing method for quality parameters of aviation kerosene in atmospheric distillation column based on least absolute shrinkage and selection operator and particle swarm optimization deep belief network (LASSO-PSO-DBN) is proposed. First, to reduce the dimension of the input variables, the least absolute shrinkage and selection operator (LASSO) algorithm is used to select the input variables that are irrelevant to the soft sensor of aviation kerosene quality parameters. Then, to improve the generalization of soft sensor model, a deep learning algorithm, deep belief network (DBN), is proposed for soft sensing of aviation kerosene quality parameters. Considering that the structure characteristics and parameters of DBN algorithm have a great impact on the learning and prediction results, the parameters of DBN are optimized based on particle swarm optimization (PSO) algorithm. The benchmark data sets and the industrial atmospheric distillation column data are used for simulation analysis and evaluation of the soft-sensing performance. The simulation results show that the novel proposed algorithm can effectively reduce the dimension of the input variables and simplify the structure of the soft sensor model. It also has good generalization ability and the predicted value is in good agreement with the actual measured value.

Continuous motion estimation of lower limbs based on deep belief networks and random forest.

Due to the lag problem of traditional sensor acquisition data, the following movement of exoskeleton robots can affect the comfort of the wearer and even the normal movement pattern of the wearer. In order to solve the problem of lag in exoskeleton motion control, this paper designs a continuous motion estimation method for lower limbs based on the human surface electromyographic (sEMG) signal and achieves the recognition of the motion intention of the wearer through a combination of the deep belief network (DBN) and random forest (RF) algorithm. First, the motion characteristics of human lower limbs are analyzed, and the hip-knee angle and sEMG signal related to lower limb motion are collected and extracted; then, the DBN is used in the dimensionality reduction of the sEMG signal feature values; finally, the motion intention of the wearer is predicted using the RF model optimized by the genetic algorithm. The experimental results show that the root mean square error of knee and hip prediction results of the combined algorithm proposed in this article improved by 0.2573° and 0.3375°, respectively, compared to the algorithm with dimensionality reduction by principal component analysis, and the single prediction time is 0.28 ms less than that before dimensionality reduction, provided that other conditions are exactly the same.

High Spatiotemporal Resolution PM2.5 Concentration Estimation with Machine Learning Algorithm: A Case Study for Wildfire in California

As an aggregate of suspended particulate matter in the air, atmospheric aerosols can affect the regional climate. With the help of satellite remote sensing technology to retrieve AOD (aerosol optical depth) on a global or regional scale, accurate estimation of PM2.5 concentration has become an important task to quantify the spatiotemporal distribution of AOD and PM2.5. However, due to the limitations of satellite platforms, sensors, and inversion algorithms, the spatiotemporal resolution of current major AOD products is still relatively low. Meanwhile, for the impact of cloud, the AOD products often have a serious data gap problem, which also objectively limits the spatiotemporal coverage of predicted PM2.5 concentration. Therefore, how to effectively improve the spatiotemporal resolution and coverage of PM2.5 concentration under the requisite accuracy is still a grand challenge. In this study, the fused high spatial-temporal resolution AOD data in our previous study were used to estimate the ground PM2.5 concentration through machine learning algorithms, the deep belief network (DBN). The PM2.5 data had spatiotemporal autocorrelation in geostatistics and followed the Gaussian kernel distribution. Hence, the autocorrelation model modified by Gaussian kernel function integrated with DBN algorithm, named Geoi-DBN, was used to estimate PM2.5 concentration. The cross-validation results showed that the Geoi-DBN (R2 = 0.86, RMSE = 6.84 µg m−3) performed better than the original DBN (R2 = 0.67, RMSE = 10.46 µg m−3). The final high quality PM2.5 concentration data can be applied for urban air quality monitoring and related PM2.5 exposure risk assessment such as wildfire.

Applying Artificial Intelligence and Deep Belief Network to predict traffic congestion evacuation performance in smart cities

This work is developed to discuss the feasibility and efficiency of adopting Artificial Intelligence (AI) Deep Learning in smart city scenarios. A traffic flow prediction model is constructed based on the Deep Belief Network (DBN) algorithm. The target road section and its historical traffic flow data in Tianjin are collected and pre-processed. Then, several Restricted Boltzmann Machines (RBM) are stacked together to form a DBN, which is trained as a generative model. Finally, its performance is analyzed by the simulation experiment. The algorithm model proposed is compared with Neuro Fuzzy C-Means (FCM) model, Deep Learning Architecture (DLA), and Convolutional Neural Network (CNN) model. The results show that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the algorithm model proposed are 4.42%, 6.21%, and 8.03%, respectively. Its prediction accuracy is significantly higher than that of the other three algorithms. In addition, the algorithm can effectively suppress the spread of congestion in the smart city, achieving timely evacuation of traffic congestion. In short, the constructed Deep Learning-based traffic flow prediction model has a high-precision prediction effect and traffic congestion evacuation performance, which can provide experimental references for the later construction of smart cities.

Application of Distributed Probability Model in Sports Based on Deep Learning: Deep Belief Network (DL-DBN) Algorithm for Human Behaviour Analysis.

With the increased development of information technology, almost all the sectors have been developed. Age, educational qualifications, gender, and other factors have no bearing on acquiring knowledge in information technology.Most humans use mobile phones and other gadgets to make their lives easier. Machine Learning techniques are used to analyse the given data and aid in the classification or prediction of the dataset depending on the problem statement. It is significant to determine human behaviour analysis in the context of sports. In this research, the Deep Learning-Deep Belief Network (DL-DBN) algorithm is implemented with probability to analyse human behaviour in sports and implement a distributed probability model for classifying the behavior. The classification results have shown that the accuracy for strength training is both the maximum and the smallest, reaching 99% and 71%, respectively.

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran.

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.