Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

Huge amounts of multimodal content and comments in a mixture form of text, image, and emoji are continuously shared by users on various social networks. Most of the comments of the users in these networks have emotional aspects, which make the multimodal sentiment analysis (MSA) an important and attractive research topics in this area. In this paper, an ensemble transfer learning method is exploited to propose a hybrid MSA model based on weighted convolutional neural networks. The extended Dempster–Shafer (Yager) theory is also utilized in the proposed method of this paper to fuse the outputs of text and image classifiers to determine the final polarity at the decision level. The pre-trained VGG16 network is firstly used to extract visual features and fine-tune on the MVSA-Multiple and T4SA datasets for image sentiment classification. The Mask-RCNN model is then exploited to determine the objects in the images and convert them to text. The BERT model receives the output of this step along with the textual descriptions of the images for extracting the text features and embedding the words. The output of the BERT model is then imported into a weighted convolutional neural network ensemble (WCNNE). The texts are classified by several weak learners using the AdaBoost that is an ensemble learning technique in which, classifiers are trained sequentially. The combined use of several weak classifiers results in a strong classification. The WCNNE improves the performance and increases the accuracy of the results. As a fusing phase at the decision level, the outputs of the VGG16 and the WCNNE models will be finally merged using the extended Dempster-Shafer theory to obtain the correct sentiment label. The results of the experiments on the MVSA-Multiple and T4SA datasets show that the proposed model is better than the other compared methods and achieved an appropriate accuracy of 0.9348 on MVSA and 0.9689 on the T4SA datasets. Moreover, the proposed model reduces training time due to the use of transfer learning and the proposed AdaBoostCNN achieves better results compared to the single CNN. 

Ensemble transfer learning-based multimodal sentiment analysis using weighted convolutional neural networks

An essential work in natural language processing is the Multi-Label Text Classification (MLTC). The purpose of the MLTC is to assign multiple labels to each document. Traditional text classification methods, such as machine learning usually involve data scattering and failure to discover relationships between data. With the development of deep learning algorithms, many authors have used deep learning in MLTC. In this paper, a novel model called Spotted Hyena Optimizer (SHO)-Long Short-Term Memory (SHO-LSTM) for MLTC based on LSTM network and SHO algorithm is proposed. In the LSTM network, the Skip-gram method is used to embed words into the vector space. The new model uses the SHO algorithm to optimize the initial weight of the LSTM network. Adjusting the weight matrix in LSTM is a major challenge. If the weight of the neurons to be accurate, then the accuracy of the output will be higher. The SHO algorithm is a population-based meta-heuristic algorithm that works based on the mass hunting behavior of spotted hyenas. In this algorithm, each solution of the problem is coded as a hyena. Then the hyenas are approached to the optimal answer by following the hyena of the leader. Four datasets are used (RCV1-v2, EUR-Lex, Reuters-21578, and Bookmarks) to evaluate the proposed model. The assessments demonstrate that the proposed model has a higher accuracy rate than LSTM, Genetic Algorithm-LSTM (GA-LSTM), Particle Swarm Optimization-LSTM (PSO-LSTM), Artificial Bee Colony-LSTM (ABC-LSTM), Harmony Algorithm Search-LSTM (HAS-LSTM), and Differential Evolution-LSTM (DE-LSTM). The improvement of SHO-LSTM model accuracy for four datasets compared to LSTM is 7.52%, 7.12%, 1.92%, and 4.90%, respectively.

/pdf/a-new-hybrid-based-on-long-short-term-memory-network-with-2gv9kspz.pdf

A New Hybrid Based on Long Short-Term Memory Network with Spotted Hyena Optimization Algorithm for Multi-Label Text Classification

A survey of image encryption algorithms based on chaotic system

Most of the existing log anomaly detection methods suffer from scalability and numerous false positives. Besides, they cannot rank the severity level of abnormal events. This paper proposes a log anomaly detection based on Q-learning, namely QLLog, which can detect multiple types of system anomalies and rank the severity level of abnormal events. We first build a mathematical model of log anomaly detection, proving that log anomaly detection is a sequential decision problem. Second, we use the Q-learning algorithm to build the core of the anomaly detection model. This allows QLLog to automatically learn directed acyclic graph log patterns from normal execution and adjust the training model according to the reward value. Then, QLLog combines the advantages of the Q-learning algorithm and the specially designed rules to detect anomalies when log patterns deviate from the model trained from log data under normal execution. Besides, we provide a feedback mechanism and build an abnormal level table. Therefore, QLLog can adapt to new log states and log patterns. Experiments on real datasets show that the method can quickly and effectively detect system anomalies. Compared with the state of the art, QLLog can detect numerous real problems with high accuracy 95%, and its scalability outperforms other existing log-based anomaly detection methods.

QLLog: A log anomaly detection method based on Q-learning algorithm

https://www.techscience.com/csse/v37n1/41435/pdf

A Generative Adversarial Networks for Log Anomaly Detection

OILog: An online incremental log keyword extraction approach based on MDP-LSTM neural network

The invention discloses an SaaS software fault diagnosis method based on a convolutional neural network. The method comprises the following steps: firstly, filtering log data generated by a system; then, subjecting the filtered log data to category classification and labeling; carrying out denoising processing on the log data subjected to category division and labeling; secondly, vectorizing the denoised log data by adopting a Skip-Gram method, and constructing a two-dimensional topological structure for the vectorized log data to form two-dimensional vector data of two dimensions, namely a word vector dimension and a word dimension; training a pre-constructed convolutional neural network model by taking the constructed two-dimensional vector data as training data to obtain a trained performance fault diagnosis model; and finally, diagnosing the log data of the unknown type by utilizing the trained performance fault diagnosis model to obtain the fault type of the log data. According tothe invention, the diagnosis accuracy and diagnosis efficiency of log data of unknown types are improved.

SaaS software fault diagnosis method and device based on convolutional neural network

The invention discloses a performance fault detection method based on a support vector machine, and the method mainly comprises the steps: carrying out the preprocessing of log data, and carrying outthe construction and training of a prediction model, and the preprocessing of the log data comprises the vectorization processing of the performance log data generated by a system; Labeling the log data subjected to vectorization processing to obtain a training data set; The construction and training of the prediction model specifically comprise the steps of selecting a kernel function through a Gaussian kernel and linear kernel direct product mixing mode, and selecting a parameter and a penalty factor of the kernel function based on a bilinear mode search method; Constructing a prediction model based on a support vector machine according to the kernel function, the parameters of the kernel function and a penalty factor, and training the prediction model by using the training data set; Andfinally, detecting the to-be-detected log data by adopting the trained prediction model. The technical effect of improving the prediction efficiency and the prediction accuracy is achieved.

A performance fault detection method and device based on a support vector machine

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