Research on Information Visualization Graphic Design Teaching Based on DBN Algorithm.

This paper work is focused on Performance comparison of intrusion detection system between DBN Algorithm and SPELM Algorithm. Researchers have used this new algorithm SPELM to perform experiments in the area of face recognition, pedestrian detection, and for network intrusion detection in the area of cyber security. The scholar used the proposed State Preserving Extreme Learning Machine(SPELM) algorithm as machine learning classifier and compared it's performance with Deep Belief Network (DBN) algorithm using NSL KDD dataset. The NSL- KDD dataset has four lakhs of data record; out of which 40% of data were used for training purposes and 60% data used in testing purpose while calculating the performance of both the algorithms. The experiment as performed by the scholar compared the Accuracy, Precision, recall and Computational Time of existing DBN algorithm with proposed SPELM Algorithm. The findings have show better performance of SPELM; when compared its accuracy of 93.20% as against 52.8% of DBN algorithm;69.492 Precision of SPELM as against 66.836 DBN and 90.8 seconds of Computational time taken by SPELM as against 102 seconds DBN Algorithm.

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.

Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods

With the continuous development of network technology and information technology, more and more attention has been paid to the network security of smart meters. When facing the network intrusion data of smart meters, the data has large-scale, high-dimensional and non-linear intrusion detection problems. This paper proposes a model about the network intrusion model, which is based on Adaptive Particle Optimization (APSO) Deep Belief Network (DBN). First, construct a classification model based on DBN algorithm. Next, accelerate the optimization of DBN by using APSO algorithm, and calculate the number of hidden layer nodes to obtain the DBN model structure of the best hidden layer. Finally, construct the intrusion detection model of smart meter based on APSO-DBN.

To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswana, and Cuprite hyperspectral datasets are selected as research subjects in this study, and the objective is to process remote-sensing hyperspectral images via machine learning to realize the automatic and intelligent classification of features. Then, the basic principles of the support vector machine (SVM) and extreme learning machine (ELM) classification algorithms are introduced, and they are applied to the datasets. Next, by adjusting the parameter estimates using a restricted Boltzmann machine (RBM), a new terrain classification model of hyperspectral images that is based on a deep belief network (DBN) is constructed. Next, the SVM, ELM, and DBN classification algorithms for hyperspectral image terrain classification are analysed and compared in terms of accuracy and consistency. The results demonstrate that the average detection accuracies of ELM on the three datasets are 89.54%, 96.14%, and 96.28%, and the Kappa coefficient values are 0.832, 0.963, and 0.924; the average detection accuracies of SVM are 88.90%, 92.11%, and 91.68%, and the Kappa coefficient values are 0.768, 0.913, and 0.944; the average detection accuracies of the DBN classification model are 92.36%, 97.31%, and 98.84%, and the Kappa coefficient values are 0.883, 0.944, and 0.972. The results also demonstrate that the classification accuracy of the DBN algorithm exceeds those of the previous two methods because it fully utilizes the spatial and spectral information of hyperspectral remote-sensing images. In summary, the DBN algorithm that is proposed in this study has high application value in object classification for remote-sensing hyperspectral images.

A severe problem that regularly affects cloud systems are intrusions. Ignore how the expansion of Internet of Things (IoT) devices will result in enormous intrusions. To distinguish intrusions from authorized network activity, detection is a crucial procedure. An Enhanced Lion Optimization Algorithm (ELOA) is utilized in this research, IoT intrusion detection system. Intrusions are classified using the Deep Belief Network (DBN) and an SDN controller technique. The proposed ELOA-based Intrusion Detection System uses the optimal weight in DBN to train the neurons to categorize the data in a network as normal and attacked during the training phase. In the testing step that follows training, data from nodes are examined, and by contrasting the training results, they are categorized as normal and attacked data. By using the proposed ELOA and DBN algorithms, our intrusion detection system can successfully identify intrusions. Based on the creation of blacklists for detecting IoT intrusions, the (SDN) Software Defined Networking controller can effectively prohibit harmful devices. In order to demonstrate that the proposed ELOA finds network intrusions more successfully, its performance is compared to that of other existing techniques. The node sizes of the algorithms are run and evaluated for 1000, 2000, 3000, 4000, and 5000 respectively. At highest node 5000, the Proposed ELOA and DPN have precision, recall, f-score and accuracy becomes as 97.8, 96.22, 97.5 and 98.67 respectively.

Using electroencephalogram (EEG) signals to control intelligent wheelchairs is one of the new control methods for controlling intelligent wheelchairs. The most serious problem in the control process is recognition and classification of EEG signals, especially a variety of EEG signals. To improve the accuracy of EEG classification, improved-common spatial patterns and a deep-belief network algorithm are proposed and used for recognition and classification of EEG signals. A variety of different EEG signals were collected by an Emotiv EPOC+ EEG collector, and the characteristic signals were extracted by improved-common spatial patterns and identified and classified by a deep-belief network algorithm. Simulation results show that the classification accuracy of improved-common spatial patterns and the deep-belief network algorithm is higher than that of the traditional classification method, and a research perspective is provided for the classification of various EEG signals in the future.

The global rise in heart disease necessitates precise prediction tools to assess individual risk levels. This paper introduces a novel Multi-Objective Artificial Bee Colony Optimized Hybrid Deep Belief Network and XGBoost (HDBN-XG) algorithm, enhancing coronary heart disease prediction accuracy. Key physiological data, including Electrocardiogram (ECG) readings and blood volume measurements, are analyzed. The HDBN-XG algorithm assesses data quality, normalizes using z-score values, extracts features via the Computational Rough Set method, and constructs feature subsets using the Multi-Objective Artificial Bee Colony approach. Our findings indicate that the HDBN-XG algorithm achieves an accuracy of 99%, precision of 95%, specificity of 98%, sensitivity of 97%, and F1-measure of 96%, outperforming existing classifiers. This paper contributes to predictive analytics by offering a data-driven approach to healthcare, providing insights to mitigate the global impact of coronary heart disease.

Neural networks have been widely used in color space conversion in the digital printing process. The shallow neural network easily obtains the local optimal solution when establishing multi-dimensional nonlinear mapping. In this paper, an improved high-precision deep belief network (DBN) algorithm is proposed to achieve the color space conversion from CMYK to L*a*b*. First, the PANTONE TCX color card is used as sample data, in which the CMYK value of the color block is used as input and the L*a*b* value is used as output; then, the conversion model from CMYK to L*a*b* color space is established by using DBN. To obtain better weight and threshold, DBN is optimized by a particle swarm optimization algorithm. Experimental results show that the proposed method has the highest conversion accuracy compared with Back Propagation Neural Network, Generalized Regression Neural Network, and traditional DBN color space conversion methods. It can also adapt to the actual production demand of color management in digital printing.

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.

Image-driven machine learning for automatic characterization of grain size and distribution in smart vanadium dioxide thin films

A 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.

Recently, the internet of things (IoT) has evolved into a breakthrough for creating intelligent settings. Any technology's reliance on the IoT model is seen as having major security and privacy issues. The various conceivable attacks carried out by intruders give rise to privacy and security considerations. Therefore, creating an intrusion detection system is crucial for identifying attacks and anomalies in the IoT system. In this work, a deep belief network (DBN) algorithm model for the intrusion detection system has been proposed. The CICIDS 2017 dataset is used for the performance analysis of the current IDS model in terms of assaults and anomaly detection. Accuracy, recall, precision, F1-score, detection rate, and other characteristics were all improved by the proposed method. IoT technology has revolutionized how healthcare is provided to patients. Medical institutions are very concerned about IoT security because of the network enabled IoT devices' integration with healthcare network infrastructure.

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%.

The license plate of the vehicle is unique and is only owned by one vehicle per vehicle plate series, to make it easier for the police, especially the traffic police, to track traffic violators through the vehicle number plate. The Deep Belief Network algorithm works by processing the dataset through 3 stages, where the first layer is trained, the results of the first layer are then re-trained, and the results of the second layer calculation are made into the third layer count, the mean results on the calculation of the third layer become the result of learning Deep Belief Network then with the Template Matching algorithm, Deep Belief Network is assisted with the introduction of vehicle plates. In a study conducted using the DBN algorithm with the Template Matching method succeeded in recognizing a vehicle plate with a success percentage of 80% from 20 trials. The experiments carried out included plates that were not clearly seen. Failures that occur in the trials are generally due to under- or over-lighting on the vehicle plate.