Application of improved convolution neural network in financial forecasting

Financial status and its role in the national economy have been increasingly recognized. In order to deduce the source of monetary funds and determine their whereabouts, financial information and prediction have become a scientific method that can not be ignored in the development of national economy. This paper improves the existing CNN and applies it to financial credit from different perspectives. Firstly, the noise of the collected data set is deleted, and then the clustering result is more stable by principal component analysis. The observation vectors are segmented to obtain a set of observation vectors corresponding to each hidden state. Based on the output of PCA algorithm, we recalculate the mean and variance of all kinds of observation vectors, and use the new mean and covariance matrix as credit financial credit, and then determine the best model parameters.The empirical results based on specific data from China's stock market show that the improved convolutional neural network proposed in this paper has advantages and the prediction accuracy reaches.

DPDRC, a Novel Machine Learning Method about the Decision Process for Dimensionality Reduction before Clustering

This paper examines the critical decision process of reducing the dimensionality of a dataset before applying a clustering algorithm. It is always a challenge to choose between extracting or selecting features. It is not obvious to evaluate the importance of the features since the most popular methods to do it are usually intended for a supervised learning technique process. This paper proposes a novel method called “Decision Process for Dimensionality Reduction before Clustering” (DPDRC). It chooses the best dimensionality reduction method (selection or extraction) according to the data scientist’s parameters and the profile of the data, aiming to apply a clustering process at the end. It uses a Feature Ranking Process Based on Silhouette Decomposition (FRSD) algorithm, a Principal Component Analysis (PCA) algorithm, and a K-means algorithm along with its metric, the Silhouette Index (SI). This paper presents five scenarios based on different parameters. This research also aims to discuss the impacts, advantages, and disadvantages of each choice that can be made in this unsupervised learning process.

A PROPOSED SIGN LANGUAGE MODEL FOR SPEECHLESS PERSONS USING EEG SIGNALS

Recently, algorithms of machine learning are widely used with the field of electroencephalography (EEG)-Brain-Computer interfaces (BCI). In this paper, a sign language software model based on the EEG brain signal was implemented, to help the speechless persons to communicate their thoughts to others. The preprocessing stage for the EEG signals was performed by applying the Principle Component Analysis (PCA) algorithm to extract the important features and reducing the data redundancy. A model for classifying ten classes of EEG signals, including Facial Expression(FE) and some Motor Execution(ME) processes, had been designed. A neural network of three hidden layers with deep learning classifier had been used in this work. Data set from four different subjects were collected using a 14 channels Emotiv epoc+ device. A classification results with accuracy 95.75% were obtained ‎for the collected samples. An optimization process was performed on the predicted class with the aid of user, and then sign class will be connected to the specified sentence under a predesigned lock up table.

Ensemble Classification Approach for Sarcasm Detection

Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K -means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.

Research and analysis of music development based on k-means and PCA algorithm

The purpose of this article is to establish an algorithm model that can measure the influence of music, capture the evaluation index reflecting the influence of music, and extend the model to other fields such as politics, culture, and society. We have established a music influence-oriented network algorithm model based on influencers and followers, where each artist is a node, and each follower is a connection between artists. We define relative interaction strength indicators to help understand the entire network algorithm. In addition, we also used time, genre and other scales to further optimize the network algorithm. We first use the PCA algorithm to determine indicators that reflect music similarity, such as vitality, activity, popularity, overall loudness, etc. On this basis, an evaluation algorithm model based on cosine similarity is established to calculate music similarity values of different genres. In addition, we use the K-MEANS algorithm to normalize each feature index and sum its variance. Finally, we noticed that the similarity of artists within genres is higher than the similarity of artists between genres. We further analyzed the differences and influences within and between genres. Taking time as a distinction, a relative heat map of the interactive influence of genres is drawn. It is understood that certain genres will obviously have a certain influence over time. We summarize this model as an impact correlation analysis model. First, we choose a representative influencer. Then, based on the cosine similarity, we obtained the music similarity with the fans in batches, thus more intuitively concluded that the Internet celebrities did affect the respective artists. In addition, we combined the calculation of SPSS variance and selected different indicators to visualize the radar chart to understand the attractiveness differences of certain music features. We first select the musical characteristics with obvious changing trends, then locate the position of the changer in the music evolution process through the time distribution diagram of the corresponding work, and finally select the representative changer. We analyzed the change history of each indicator in the selected genre over time, and finally got the global directed network diagram. Based on the network algorithm model established in the previous question, we analyzed the background of the times and found that there is an interaction between music and the cultural environment. Finally, we also analyzed the advantages and disadvantages of the algorithm model, and discussed the application of the method in other fields.

Copy-Move Forgery Detection Based on Euclidean Distance and Texture Feature Analysis

Digital images are important part of our life. Copy and Move forgery detection techniques are designed to detect edited part of the image. The copy and move forgery techniques are based on the feature detection and matching. The techniques which are designed so far use the Euclidean distance concept for feature matching. The feature detection techniques which are much popular like Haar transformation are used for feature extraction. In this research, the PCA algorithm is used for the simplification of features which are extracted with Haar transformation. The GLCM algorithm is used for texture feature analysis of input image. In the end, Euclidean distance is used for feature matching and mismatched features are marked as forgery. The proposed approach is implemented in MALTAB and results are analyzed in terms of accuracy.

Robust face recognition based on a new Kernel-PCA using RRQR factorization

In the last ten years, many variants of the principal component analysis were suggested to fight against the curse of dimensionality. Recently, A. Sharma et al. have proposed a stable numerical algorithm based on Householder QR decomposition (HQR) called QR PCA. This approach improves the performance of the PCA algorithm via a singular value decomposition (SVD) in terms of computation complexity. In this paper, we propose a new algorithm called RRQR PCA in order to enhance the QR PCA performance by exploiting the Rank-Revealing QR Factorization (RRQR). We have also improved the recognition rate of RRQR PCA by developing a nonlinear extension of RRQR PCA. In addition, a new robust RBF Lp-norm kernel is proposed in order to reduce the effect of outliers and noises. Extensive experiments on two well-known standard face databases which are ORL and FERET prove that the proposed algorithm is more robust than conventional PCA, 2DPCA, PCA-L1, WTPCA-L1, LDA, and 2DLDA in terms of face recognition accuracy.

Development of a dynamic intelligent recognition system for a real-time tracking robot

<p>Nowadays, the development of computer vision technology help to overcome track and identify humans within a location in the complex environment through mobile robots, which gives the motivation to presents a vision-based approach to a mobile security robot. The proposed system utilizes a wireless camera to detect the objects in the field of robot view. Principle component analysis (PCA) algorithm and filters are used to implement and demonstrate the process of the images. This gives the designed system the ability to recognize objects independently from current light conditions. Frame tracking in the images uses an attention system to get an estimate of the position of a person. This estimate helps the applied camera to identify objects with changing background lighting conditions such as a fire inside a building. By using this estimate, the applied camera could identify objects with changing background lighting conditions such as a fire inside premises. The system has been tested using the MATLAB environment, and the empirical performance explains the efficiency and strongness of the suggested device.</p>

Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm

Automated detection of brain tumor location is essential for both medical and analytical uses. In this paper, we clustered brain MRI images to detect tumor location. To obtain perfect results, we presented an unsupervised robust PCA algorithm to clustered images. The proposed method clusters brain MR image pixels to four leverages. The algorithm is implemented for five brain diseases such as glioma, Huntington, meningioma, Pick, and Alzheimer’s. We used ten images of each disease to validate the optimal identification rate. According to the results obtained, 2% of the data in the bad leverage part of the image were determined, which acceptably discerned the tumor. Results show that this method has the potential to detect tumor location for brain disease with high sensitivity. Moreover, results show that the method for the Glioma images has approximately better results than others. However, according to the ROC curve for all selected diseases, the present method can find lesion location.

A Boosted 3-D PCA Algorithm Based on Efficient Analysis Method

In this paper, we propose a boosted 3-D PCA algorithm based on an efficient analysis method. The proposed method involves three steps that improve image detection. In the first step, the proposed method designs a new analysis method to solve the performance problem caused by data imbalance. In the second step, a parallel cross-validation structure is used to enhance the new analysis method further. We also design a modified AdaBoost algorithm to improve the detector accuracy performance of the new analysis method. In order to verify the performance of this system, we experimented with a benchmark dataset. The results show that the new analysis method is more efficient than other image detection methods.