查看更多>>摘要：For several years the modeling as well as forecasting of the prices of stocks have been extremely challenging for the business community and researchers as a result of the existence of noise in samples and also the non-stationary behaviour of information samples. Notwithstanding these drawbacks with improved deep learning, it is now possible to design schemes that will efficiently perform the feature learning task. For this work, we proposed a brand-new end to end algorithm labeled EHTS toward solving the stock price forecasting problem. The AB - CNN and CB - LSTM modules extract features from the stock price dataset and soon after amalgamating the results. Thus, the output of the concatenation stage was feed into the concluding stage which is a stand-alone MLP module. The inclusion of the LSTM and Attention Mechanism in our architecture is to extract long-range and exceptionally long-term stock price information. We experiment the proposed algorithm on two popular stocks both from the NYSE stock market namely "Johnson & Johnson" code-named, "JNJ" and the Bank of America (BAC). In terms of the rMSE, MAE and MAPE error metrics, our proposed scheme gives the lowest error value in all for all datasets. Also, five percentage training window sizes are experimented and EHTS outperforms all the baseline schemes for the different window sizes in all the two datasets with the 70% window size having the highest performance. In terms of number of epochs, EHTS uses the lowest number of epochs for training than the other schemes in all the datasets. Finally, we as well study our stock's information to point out short-range trading opportunities by performing simulations on our stock price data. The metrics considered in the simulation are as follows: Moving Average (MA), Moving Average Convergence Divergence (MACD) curve, MACD histogram, Signal line, Relative Strength Index (RSI), Returns (R), Annual Returns (AR), Sharpe Ratio (SR), Annual Volatility (V), Maximum DrawDown (MDD) and Daily WinningRate (DWR). For all the aforementioned metrics, EHTS performs better than the baselines. Experimental results revealed that our proposed scheme outperformed the stand-alone deep learning schemes, statistical algorithms, and machine learning models from the beginning to the end. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：In multi-label learning algorithms, the classification performance can be significantly improved using global and local label correlation. However, the incompleteness of the label space leads to difficulties in measuring the label correlation. In the process of label recovery, many multi-label learning algorithms focus on label correlation, but ignore the queried instance information. In this paper, we introduce an attention mechanism to jointly exploit label and instance information in order to improve the quality of the recovered labels. Firstly, the attention mechanism is used to encode the label and the instance information for label space reconstruction. Secondly, attention computations are performed on the reconstructed label space to obtain the label completion matrix. Finally, global and local features of label correlation are used to improve the model robustness, and label prediction is completed. Through the analysis of the experimental results of multiple benchmark multi-label datasets, it is demonstrated that the proposed method has certain advantages over other state-of-the-art algorithms. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：The event-triggered security control and fault detection for nonlinear systems, in which the output signals are wirelessly transmitted to the control and detection module, is investigated in this paper. In particular, quantization before data transmission and deception attacks during data transmission are considered. To ease the data transmission pressure of wireless networks, a dynamic event-triggered protocol is proposed. Specifically, the triggering threshold changes in accordance with the system state. A stochastic sequence is introduced for modelling the time of occurrence of cyber-attacks within an insecure communication environment. By taking the dynamic event-triggered protocol, signal quantization and randomly occurring deception attacks into account, an integrated dynamic output feedback controller and fault detection filter module is developed. With the proposed design, stochastic stability is obtained, while the expected levels of security control performance and detection performance are also guaranteed. Novel algorithms are proposed for determining the parameters of the controller, filter and dynamic event-triggered protocol. A practical example is given to demonstrate the effectiveness of the proposed design idea. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：With the surge of the large volume of data availability, Machine Learning and mainly Deep Learning techniques are the leading solutions in classification and predictive tasks, targeted at data-efficient learning. These models learn by training on many diversified samples in a process that is computationally expensive or time-consuming. Moreover, in many real-world scenarios, the amount of available data for training is unsuitable, because it is unlabeled or covers only portions of the whole reference domain cases. This paper proposes an alternative approach for document classification that leverages the distribution of the data projected in the multi-dimensional feature space to assess the weight of features in the final classification. The approach does not rely on traditional iterative methods for classification but builds a relevance measure to assess the relevance/importance of the features describing the domain of interest. The idea is to harness this metric to select relevant features and then express the values calculated by these metrics in natural language by exploiting fuzzy variables and linguistic labels to make human comprehension more immediate. The approach has been employed for emotion extraction from social media messages. The novelty of this approach is twofold: first, the well-known TF-IDF measure was reinterpreted as a relevance measure of emotions discovered in text content. Then, the discovered emotion relevance was described by fuzzy linguistic labels, defined on an ad-hoc-designed fuzzy partition, to express the data classification in natural language, more suitable to human understanding. (C) 2022 Elsevier Inc. All rights reserved.
Abd Elaziz, MohamedAbu-Donia, Hassan M.Hosny, Rodyna A.Hazae, Saeed L....
19页
查看更多>>摘要：This paper establishes an innovative approach of rough set (RS) approximations, namely the extension of knowledge based on the rough approximation (EKRA), which generalizes the old concepts and gets preferable results by reducing the boundary regions. In contrast to the former RS methods that obtained upper and lower approximations by several methods for special cases of binary relations. In addition, to assess the applicability of this approach it is combined with LSHADE with semi-parameter adaptation combined with CMA-ES (LSHADE-SPACMA) as a feature selection method, where EKRA is used as an objective function. The developed FS approach, named, LSPEKRA, which depends on LSHADE-SPACMA and EKRA aims to find the relevant features. This leads to improving the classification of different datasets. The experimental results show the great performance of the presented method against other Evolutionary algorithms. In addition, the FS methods based on EKRA provide results better than traditional RS in terms of performance measures. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Echo state network (ESN) is an effective tool for nonlinear systems modeling. To handle irregular noises or outliers in practical systems and alleviate the overfitting issue, the robust echo state network with sparse online learning (RESN-SOL) is proposed. Firstly, the epsilon-insensitive loss function is introduced to replace the commonly used quadratic loss function, which is theoretically optimal for Gaussian noise distribution. Secondly, the online gradient descent algorithm is used to calculate the network readout. Notably, the better learning performance can be achieved by the constant learning rate rather than the decreasing step size. Based on this observation, the sparse online learning algorithm (SOL) is proposed, in which the constant step size is used. Particularly, the SOL is able to truncate the small weights in network readout to zero for achieving sparsity. Furthermore, the convergence of RESN-SOL is theoretically analyzed, which implies the tradeoff between learning performance and readout sparsity can be controlled by a predefined sparsity parameter. Finally, the proposed method is verified in two simulated benchmarks and an actual dynamical wastewater treatment system. Experimental results demonstrate that the RESN-SOL exhibits better robustness against outliers, network compactness and modeling accuracy than other existing algorithms. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Negative emotional contagion along with sentiment mutation through information propagation on social media is critical for mitigating disinformation and directing public opinion for compliance with key public interventions, such as vaccine uptake during a pandemic. Here, we develop a dynamic multiple negative emotional susceptible-forwarding-immune (MNE-SFI) model to examine how negative emotion spreads on social media and how sentiment mutation impacts by fitting the model to real multiple temporal information in messages with sentiments obtained from the Chinese Sina microblog. Emotional choices, meaning that individuals attempting to spread information are not only influenced by the objective emotions embedded in the influential information spread by influencers but also by subjective emotional tendencies, is an essential human behavior for information propagation. Hence, we seek to link the negative emotional contagion in the network at the macroscopic level to the emotional choices of individuals, and model parameters are used at the microcosmic level to measure the "copying" and "mutation" probabilities of negative sentiments in an event. Our results illustrate the emotional choices of users play essential roles in methods for mitigating harmful emotion spread and promoting meaningful emotion diffusion. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Fetal heart rate (FHR) monitoring is an important medical-assisted diagnostic technique widely used by clinicians to assess fetal well-being. However, one challenge is that the capabilities of such diagnostic algorithms often rely on an enormous quantity of labeled clinical data to train a model, which do not preserve patient privacy. The high performance of such diagnostic algorithms is further hindered by category imbalance problems. Therefore, the general objective of this study is to develop a small-sample generation method that generates FHR signals of different physiological/pathological categories and arbitrary lengths. This study focuses on two significant impediments to the existing generation methods: the instability of generative adversarial networks (GAN) during model training, the mode collapse problem and the subsequent training of different specific models for different data categories, which contributes to high model training costs. To address these problems, we propose a novel generative adversarial architecture, referred to as CCWGAN-GP, based on a deep neural network optimized by the Wasserstein distance with gradient penalty, and incorporate an auxiliary classifier as a category constraint to enrich the diversity of generated data. The proposed method is comprehensively evaluated using 200 real FHR recordings from four aspects: training performance, the fidelity and diversity of generated data, and the potential improvement in the classification model. Compared with training on small-sample datasets and category-imbalanced datasets, training on augmented datasets improves the accuracy by approximately 12% and 8%, respectively. The developed architecture provides a reference value for a practical solution to the FHR data imbalance and insufficient sample problems. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Domain adaptation is a fundamental research field, which focuses on transforming knowledge between different domains. With the massive growth of video data, the video domain adaptation problem becomes increasingly significant for practical tasks. Motivated by the excellent performance of Grassmann manifolds representation in video recognition tasks, we propose an optimal transport based video domain adaptation model on Grassmann manifolds. The proposed model reduces the discrepancy between different domains for the frame and video level features. First, the frame level discrepancy is reduced by extracting domain consistency features. At the video level, a fixed number of frame features are formed and represented as points on Grassmann manifolds. These points are fused with predicted labels to form fusion features. Finally, the video level discrepancy is reduced by minimizing the distribution discrepancy of the fusion features between two domains. Cross-domain video recognition experiments demonstrate the validity of the proposed model. The experimental results demonstrate the excellent performance of the proposed algorithm compared with the state-of-art video domain adaptation models. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：The property of reversibility is quite meaningful for the classic theoreticabl computer science model, cellular automata. This paper focuses on the reversibility of general one-dimensional (1D) linear cellular automata (LCA), under null boundary conditions over the finite field Z(p). Although the existing approaches have split the reversibility challenge into two sub-problems: calculate the period of reversibility first, then verify the reversibility in a period, they are still exponential in the size of the CA's neighborhood. In this paper, we use two efficient algorithms with polynomial complexity to tackle these two challenges, making it possible to solve large-scale reversible LCA, which substantially enlarge its applicability. Finally, we provide an interesting perspective to inversely generate a 1D LCA from a given period of reversibility. (C) 2022 Elsevier Inc. All rights reserved.
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