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Aiming at the limitations of existing algorithms of network intrusion detection in dealing with complex data of imbalance and high dimensionality, this paper proposes an intrusion detection algorithm based on convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM). First, the data-type conversion, oversampling technology and image data conversion are included in the data preprocessing to make the data balanced and adapt to the input format. Then, by the convolutional layer, pooling layer and fully connected layer of the CNN model, the main features are abstracted from the converted image data. Finally, data of the main features is used for training and testing the LightGBM model, so as to get the final classification results. This paper uses KDDCUP99 dataset to carry out multi-classification experiments. By comparing the experiments before and after balancing the dataset, and comparing with similar algorithms, it verifies the superiority of the proposed algorithm in the classification performance of intrusion detection, especially for the minority attack classes. [ABSTRACT FROM AUTHOR]

The clinical diagnosis of heart disease in most situations is based on a difficult amalgamation of pathological and clinical information. Because of this complication, there is a significant level of curiosity among many diagnostic healthcare professionals and researchers who are keenly interested in the efficient, accurate, and early-stage forecasting of heart disease. Deep Learning Algorithms aid in the prediction of heart disease. The main focus of this paper is to develop a method for predicting heart disease through Modified Rough K means + + (MRK + +) clustering along with the Restricted Boltzmann Machine (RBM). This paper is categorized into two modules: (1) Propose a clustering component based on Modified Rough K-means + + ; (2) disease prediction based on RBM. The input Cleveland dataset is clustered using the stochastic probabilistic rough k-means + + clustering technique in the module for clustering. The clustered data is acquired and used in the RBM, and this hybrid structure is then used in the heart disease forecasting module. Throughout the testing procedure, the most valid result is chosen from the clustered test data, and the RBM classifier that correlates to the nearest cluster in the test data is based on the smallest distance or similar parameters. Furthermore, the output value is used to predict heart disease. There are three different types of experiments that are performed: In the first experiment comprises modifying the rough K-means + + clustering algorithm, the second experiment evaluates the classification result, and the third experiment suggests hybrid model representation. When the Hybrid Modified Rough k-means + + - RBM model is compared with any single model, it provides the highest accuracy. [ABSTRACT FROM AUTHOR]

To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]

With the continuous improvement of science and technology, the substation remote control system has been constantly improved, which provides the possibility for the complete realization of intelligent and unmanned substation. However, due to the special substation environment, it is easy to cause interference, coupled with the low accuracy of today's video image processing algorithm, which leads to the frequent occurrence of false alarms and missing alarms. Manual intervention is needed to deal with this, which inhibits the display of automatic intelligent substation processing functions. Therefore, in this paper, the most rapidly developed machine learning algorithm — deep learning is applied to the substation instrument equipment identification processing, in order to improve the accuracy and efficiency of instrument equipment identification, and make due contributions to the full realization of unattended substation. [ABSTRACT FROM AUTHOR]

Aiming at the problems that the existing assessment methods are difficult to solve, such as the low efficiency and uncertainty of network security situation assessment in complex network environment, by constructing the characteristic elements of network security big data, a typical model based on deep learning, long short-term memory (LSTM), is established to assess the network security situation in time series. The hidden relationship and change trend of network security situation are automatically mined and analyzed through the deep learning algorithm of big data, which greatly improves the prediction accuracy of security situation. Experimental analysis shows that this method has a better assessment effect on network threats, has higher learning efficiency than the traditional network situation assessment methods, and has strong representation ability in the face of network threats. It can more accurately and effectively assess the changing trend of big data security situation in the future. [ABSTRACT FROM AUTHOR]

AlphaGo and its successors AlphaGo Zero and AlphaZero made international headlines with their incredible successes in game playing, which have been touted as further evidence of the immense potential of artificial intelligence, and in particular, machine learning. AlphaGo defeated the reigning human world champion Go player Lee Sedol 4 games to 1, in March 2016 in Seoul, Korea, an achievement that surpassed previous computer game-playing program milestones by IBM's Deep Blue in chess and by IBM's Watson in the U.S. TV game show Jeopardy. AlphaGo then followed this up by defeating the world's number one Go player Ke Jie 3-0 at the Future of Go Summit in Wuzhen, China in May 2017. Then, in December 2017, AlphaZero stunned the chess world by dominating the top computer chess program Stockfish (which has a far higher rating than any human) in a 100-game match by winning 28 games and losing none (72 draws) after training from scratch for just four hours! The deep neural networks of AlphaGo, AlphaZero, and all their incarnations are trained using a technique called Monte Carlo tree search (MCTS), whose roots can be traced back to an adaptive multistage sampling (AMS) simulation-based algorithm for Markov decision processes (MDPs) published in Operations Research back in 2005 [Chang, HS, MC Fu, J Hu and SI Marcus (2005). An adaptive sampling algorithm for solving Markov decision processes. Operations Research, 53, 126–139.] (and introduced even earlier in 2002). After reviewing the history and background of AlphaGo through AlphaZero, the origins of MCTS are traced back to simulation-based algorithms for MDPs, and its role in training the neural networks that essentially carry out the value/policy function approximation used in approximate dynamic programming, reinforcement learning, and neuro-dynamic programming is discussed, including some recently proposed enhancements building on statistical ranking & selection research in the operations research simulation community. [ABSTRACT FROM AUTHOR]

Deep learning-based epileptic seizure recognition via electroencephalogram signals has shown considerable potential for clinical practice. Although deep learning algorithms can enhance epilepsy identification accuracy compared with classical machine learning techniques, classifying epileptic activities based on the association between multichannel signals in electroencephalogram recordings is still challenging in automated seizure classification from electroencephalogram signals. Furthermore, the performance of generalization is hardly maintained by the fact that existing deep learning models were constructed using just one architecture. This study focuses on addressing this challenge using a hybrid framework. Alternatively put, a hybrid deep learning model, which is based on the ground-breaking graph neural network and transformer architectures, was proposed. The proposed deep architecture consists of a graph model to discover the inner relationship between multichannel signals and a transformer to reveal the heterogeneous associations between the channels. To evaluate the performance of the proposed approach, the comparison experiments were conducted on a publicly available dataset between the state-of-the-art algorithms and ours. Experimental results demonstrate that the proposed method is a potentially valuable instrument for epoch-based epileptic EEG classification. [ABSTRACT FROM AUTHOR]

In optical remote sensing image reconstruction, image registration is an important issue to address in order to ensure satisfactory reconstruction performance. In this study, a multi-frame image registration algorithm for high-resolution images and its parallel design method are proposed. The algorithm realizes an improved feature point detection method based on an adaptive gradient bilateral tensor filter and carries out weighted Gaussian surface sub-pixel interpolation to obtain more accurate corner positions, which better guarantees the registration accuracy. On this basis, multi-scale expansion is carried out to generate descriptors for image registration. In addition, the operation-level parallel analysis and design are carried out on a GPU platform based on compute unified device architecture (CUDA), and the memory model of the GPU is utilized reasonably. The task-level parallel analysis and design are carried out based on the GPU stream model. Moreover, based on the open multi-processing (OpenMP) platform, a multi-core CPU carries out parallel design at the operation level and task level, which realizes post-processing operations such as optical remote sensing images loading, accurate matching, and coordinate mapping, thereby effectively improving registration speed. Compared with feature point algorithms and deep learning algorithm, our algorithm and its parallel design significantly improve the registration accuracy and speed of high-resolution optical remote sensing images. [ABSTRACT FROM AUTHOR]

We present an empirical analysis of the source code of the Fluoride Bluetooth module, which is a part of standard Android OS distribution, by exhibiting a novel approach for classifying and scoring source code and vulnerability rating. Our workflow combines deep learning, combinatorial optimization, heuristics and machine learning. A combination of heuristics and deep learning is used to embed function (and method) labels into a low-dimensional Euclidean space. Because the corpus of the Fluoride source code is rather limited (containing approximately 12,000 functions), a straightforward embedding (using, e.g. code2vec) is untenable. To overcome the challenge of dearth of data, it is necessary to go through an intermediate step of Byte-Pair Encoding. Subsequently, we embed the tokens from which we assemble an embedding of function/method labels. Long short-term memory network (LSTM) is used to embed tokens. The next step is to form a distance matrix consisting of the cosines between every pairs of vectors (function embedding) which in turn is interpreted as a (combinatorial) graph whose vertices represent functions, and edges correspond to entries whose value exceed some given threshold. Cluster-Editing is then applied to partition the vertex set of the graph into subsets representing "dense graphs," that are nearly complete subgraphs. Finally, the vectors representing the components, plus additional heuristic-based features are used as features to model the components for vulnerability risk. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) is a fundamental task which affects the performance of its dependent task, e.g. machine translation. Lifelong machine learning (LML) is a continuous learning process, in which the knowledge base accumulated from previous tasks will be used to improve future learning tasks having few samples. Since there are a few studies on LML based on deep neural networks for NER, especially in Vietnamese, we propose a lifelong learning model based on deep learning with a CRFs layer, named DeepLML–NER, for NER in Vietnamese texts. DeepLML–NER includes an algorithm to extract the knowledge of "prefix-features" of named entities in previous domains. Then the model uses the knowledge in the knowledge base to solve the current NER task. Preprocessing and model parameter tuning are also investigated to improve the performance. The effect of the model was demonstrated by in-domain and cross-domain experiments, achieving promising results. [ABSTRACT FROM AUTHOR]