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Logistics information will have a certain impact on the price of agricultural products. Therefore, the price of agricultural products can be analyzed and predicted based on the logistics information. Firstly, based on logistics information, a basic model of agricultural product price is constructed. Then, support vector machine prediction algorithm and ensemble learning prediction method are applied to analyze the price relationship of agricultural products and emotional characteristics are added. Secondly, the price data of all kinds of agricultural products in various provinces of China are collected, and the algorithms constructed in this paper are used to test the price data. The results show that the accuracy of each model is improved after adding emotional feature index, which indicates that emotional features can better supplement the shortcomings of digital features, thereby improving the accuracy of prediction. [ABSTRACT FROM AUTHOR]

Cloud computing offer various services over the Internet based on pay-per-use concept. Therefore, many organizations have already adopted this system to attract the users with its desirable features. However, due to its design, makes it vulnerable to malicious attacks. This demands an Intrusion Detection System that can detect such attacks with high detection accuracy in cloud environment. This paper proposes a novel intrusion detection system that combines a fuzzy c means clustering (FCM) algorithm with support vector machine (SVM) to improve the accuracy of the detection system in cloud computing environment. The proposed system is implemented and compared with existing mechanisms. The NSL-KDD dataset is used for experiments. Based on performance evaluation and comparative analysis, the results obtained using this new hybrid mechanism (FCM–SVM) show that the proposed system can detect the anomalies with high detection accuracy and low false alarm rates over the existing techniques. [ABSTRACT FROM AUTHOR]

The complex signal represented by power load is affected by many factors, so the signal components are very complicated. So that, it is difficult to obtain satisfactory prediction accuracy by using a single model for the complex signal. In this case, wavelet decomposition is used to decompose the power load into a series of sub signals. The low frequency sub signal is remarkably periodic, and the high frequency sub signals can prove to be chaotic signals. Then the signals of different characteristics are predicted by different models. For the low frequency sub signal, the support vector machine (SVM) is adopted. In SVM model, air temperature and week attributes are included in model inputs. Especially the week attribute is represented by a 3-bit binary encoding, which represents Monday to Sunday. For the chaotic high frequency sub signals, the chaotic local prediction (CLP) model is adopted. In CLP model, the embedding dimension and time delay are key parameters, which determines the prediction accuracy. In order to find the optimal parameters, a segmentation validation algorithm is proposed in this paper. The algorithm segments the known power load according to the time sequence. Then, based on the segmentation data, the optimal parameters are chosen based on the prediction accuracy. Compared with a single model, the prediction accuracy of the proposed algorithm is improved obviously, which proves the effectiveness. [ABSTRACT FROM AUTHOR]

One of the important ways to enhance the environmental carrying capacity in the western region is to effectively protect the environmental carrying capacity in the western region from the perspective of environmental carrying capacity and the impact of the ecological environment. In this paper, the support vector machine regression prediction algorithm is introduced into the multi scenario simulation analysis of environmental carrying capacity in the western region, and the quantitative and qualitative analysis is carried out. On this basis, the environmental carrying capacity of the western region is predicted, and the way to protect the environmental carrying capacity is explored. The results of the example show that: the environmental carrying capacity of the western region is quite different in space, and human activities and climate change have a great impact on it. The prediction of environmental carrying capacity can promote the comprehensive benefit of it effectively, so as to promote the multi scenario simulation analysis of environmental carrying capacity in the western region. [ABSTRACT FROM AUTHOR]

In this paper, a face recognition fusion algorithm, namely WT-LLE-LSSVM, based on wavelet transform (WT) and local linear embedding (LLE) is proposed. Firstly, the face image is pre-processed, and decomposed by Wavelet Transform to obtain four components of face image; then, the LLE algorithm is carried out to extract the features from the four components respectively, and the weighted fusion is used to obtain the face recognition feature vector; Finally, least squares support vector machine (LSSVM) is conducted to learn the training samples with feature vector reduction, and the classifier of face recognition is established. In the case of different pixels, three face databases are chosen to test some main performance indexes such as recognition rate, speed and time of the algorithm. The simulation results show that compared with the traditional face recognition algorithm, the proposed algorithm can improve the face recognition rate, as well as the computing rate, and enhance the efficiency of face recognition. [ABSTRACT FROM AUTHOR]

In recent times, video analysis gains more popularity among the researchers, due to its social impact and widespread applications. In this research article, a novel algorithm is proposed to detect the multiple events in the videos. At first, histogram equalization technique is used for contrast enhancement and smoothing of the videos, which are collected from Columbia consumer video, and UCF101 databases. The histogram equalization delivers a better quality of frames without loss of any local information like edges, patches and points. Further, feature extraction is accomplished using gradient local ternary pattern, histogram of oriented gradients, and Tamura features to extract the feature vectors from the enhanced frames that speed up the training process. The extracted feature vectors are high dimension in nature, so an ensemble feature selection algorithm is proposed to select optimal features from the extracted features. The selected optimal feature sub-sets are fed to multi support vector machine classifier to classify the multiple events. Finally, the relevant events are retrieved utilizing Euclidean distance measure and the simulation result showed that the proposed algorithm achieved better performance in multi-event recognition. The proposed algorithm almost showed a maximum of 14.21 % improvement in video event classification by means of accuracy compared to the existing algorithms like two-stage neural network, multi-stream deep learning system, improved two-stream model, Concept wise Power Law Normalization (CPN) with Convolutional Neural Network (CNN), Motion based Shot Boundary Detection (MSBD) algorithm, and motion features with Recurrent Neural Network (RNN). [ABSTRACT FROM AUTHOR]

Defects are the major problems in the current situation and predicting them is also a difficult task. Researchers and scientists have developed many software defects prediction techniques to overcome this very helpful issue. But to some extend there is a need for an algorithm/method to predict defects with more accuracy, reduce time and space complexities. All the previous research conducted on the data without feature reduction lead to the curse of dimensionality. We brought up a machine learning hybrid approach by combining Principal component Analysis (PCA) and Support vector machines (SVM) to overcome the ongoing problem. We have employed PROMISE (CM1: 344 observations, KC1: 2109 observations) data from the directory of NASA to conduct our research. We split the dataset into training (CM1: 240 observations, KC1: 1476 observations) dataset and testing (CM1: 104 observations, KC1: 633 observations) datasets. Using PCA, we find the principal components for feature optimization which reduce the time complexity. Then, we applied SVM for classification due to very native qualities over traditional and conventional methods. We also employed the GridSearchCV method for hyperparameter tuning. In the proposed hybrid model we have found better accuracy (CM1: 95.2%, KC1: 86.6%) than other methods. The proposed model also presents higher evaluation in the terms of other criteria. As a limitation, the only problem with SVM is there is no probabilistic explanation for classification which may very rigid towards classifications. In the future, some other method may also introduce which can overcome this limitation and keep a soft probabilistic based margin for classification on the optimal hyperplane. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are identified as one of the most dangerous diseases and the major causes of the death around the world. One of the most common cardiac arrhythmias that have always been a concern for cardiologists is premature ventricular contractions. Regarding its abundance among all ages, prediction and diagnosis of this type of arrhythmia has particular importance. One of the most common, most non-invasive; and the least costly method for investigation of heart diseases is to record and analyze the electrocardiogram (ECG) signals. The purpose of this study is to analyze the ECG in order to classify premature ventricular contraction heartbeats. Having proposed a new technique based on evolutionary optimization for R peak detection, several methodologies, such as morphological assessment, polynomial curve fitting, discrete wavelet transform, and nonlinear analysis, are employed to extract features from ECG signal. Support vector machine (SVM) classifier with a linear kernel is used to detect the normal and PVC heart rates. In order to evaluate the proposed method, in addition to the MIT-BIH database, the experimental data is used and the methodology performance is proved for both databases. Finally, using different feature selection criteria such as fisher distinction, minimal-redundancy maximum-relevance, and SVM-based recursive feature elimination with correlation bias reduction, six features are introduced as best ones. The proposed PVC detection algorithm acquires the overall detection accuracy of 99.78%, with the sensitivity of 99.91% and specificity of 99.37%, for MIT-BIH dataset. [ABSTRACT FROM AUTHOR]

To improve the short-term power prediction accuracy, a comparative analysis of the PSO and SVM algorithm was carried out. Then, the two were combined and, the penalty factor and kernel function parameters in SVM model were optimized by the improved PSO algorithm. The SVM algorithm with optimized parameters and model were applied to predict and control and form PSO-SVM algorithm. Finally, the short-term power load was modelled and predicted based on PSO-SVM algorithm and it was compared with the conventional SVM algorithm. The results showed that the relative error of the average absolute value of PSO-SVM method was 1.62%, while the relative relative error of the average absolute value of conventional SVM using particle swarm optimization algorithm was 3.52%. It can be seen that the error adopting the new algorithm is reduced by 1.9%. It shows that the precision of the improved power load forecasting model is greatly improved. [ABSTRACT FROM AUTHOR]