Zojaji, ZahraEbadzadeh, Mohammad MehdiNasiri, Hamid
25页
查看更多>>摘要：Despite the empirical success of Genetic programming (GP) in various symbolic regression applications, GP is not still known as a reliable problem-solving technique in this domain. Non-locality of GP representation and operators causes ineffectiveness of its search procedure. This study employs semantic schema theory to control and guide the GP search and proposes a local GP called semantic schema-based genetic programming (SBGP). SBGP partitions the semantic search space into semantic schemas and biases the search to the significant schema of the population, which is gradually progressing towards the optimal solution. Several semantic local operators are proposed for performing a local search around the significant schema. In combination with schema evolution as a global search, the local in-schema search provides an efficient exploration-exploitation control mechanism in SBGP. For evaluating the proposed method, we use six benchmarks, including synthesized and real-world problems. The obtained errors are compared to the best semantic genetic programming algorithms, on the one hand, and data-driven layered learning approaches, on the other hand. Results demonstrate that SBGP outperforms all mentioned methods in four out of six benchmarks up to 87% in the first set and up to 76% in the second set of experiments in terms of generalization measured by root mean squared error. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：With the rapid growth of cyberattacks in the world wide, Tactics, Techniques & Procedures (TTPs) has become the most prevalent advanced indicator for a particular attack in cybersecurity community. However, extracting TTPs from unstructured cyber threat intelligence (CTI) can be arduous due to the large volume of the intelligence database. Although recent efforts on automatically extracting the structured TTPs from the unstructured intelligence have achieved promising results, they only employ simple statistical methods for TTP extraction and neglect the dependences among the hierarchical structure of TTPs. To solve those limitations, we proposed a novel attention-based method called Attention-based Transformer Hierarchical Recurrent Neural Network (ATHRNN) to extract the TTPs from the unstructured CTI. First of all, a Transformer Embedding Architecture (TEA) is designed to obtain high-level semantic representations of CTI and that of taxonomy of ATT & CK. Subsequently, an Attention Recurrent Structure (ARS) is developed to model the dependences between the tactical and technical labels in ATT & CK. Finally, a joint Hierarchical Classification (HC) module is developed to predict the final TTPs. Experiments of our approach on the collected dataset prove to be encouraging. The accuracies of TTPs extraction achieve 6.5% and 8.2% improvement in terms of Macro-F score and Micro-F score respectively. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：With the rapid development of wireless communication and smart devices, crowdsensing applications became popular due to their flexibility to deploy and low cost use. Incentive mechanism is one of the most important research contents in crowdsensing, about crowdsensing incentive mechanism, most existing data quality evaluation methods measure the contributions of users only in terms of data quality, and ignore to measure the sensing cost of users. This leads to the problems of different quality evaluation standards, difficult to measure the data quality and difficult to give a reasonable and effective evaluation to complex problems. However, expert-decision can effectively solve these problems and give high-quality evaluation decision for complex and numerous data results. In this paper, aiming at the shortcomings of existing research, we propose an expert-decision-based crowdsensing framework and gives the multidimensional rating for incentive mechanism based on user cost and data quality (MRAI-UCDQ), which consists of user cost evaluation model, data quality evaluation model, contribution quantification and reward distribution by analysing user sensing cost data and collected sensing data (comprehensive evaluation with quantitative and qualitative analysis). Finally, through nearly 30 days of real experiments, 159 volunteers were recruited and 7000 pieces of sensory data were collected. The result shows the MRAI-UCDQ improves the evaluation performance of data quality and stimulates the user's perceived participation. (C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：A novel higher-order context-layered recurrent pi-sigma neural network (CLRPSNN) is presented for the identification of nonlinear dynamical systems. The proposed model is the modified form of the classical pi-sigma neural network (PSNN) and contains an additional layer (known as the context layer) of the context nodes. Pi-sigma networks involve a product operator/unit in their output layer which indirectly incorporates in them the capability of higher-order networks and also reduces their network complexity. For tuning the weights of the proposed CLRPSNN model, a learning procedure is developed by combining the Back-Propagation (BP) and Lyapunov-stability method. The performance of the proposed model is compared with other models such as PSNN, Feed-forward neural network (FFNN) (containing single hidden layer), and various popular recurrent neural network (RNN) like Elman recurrent neural network (ERNN), Jordan recurrent neural network (JRNN), Diagonal recurrent neural network (DRNN), and a deep neural network (DNN). The simulation study showed that the proposed model has given better results as compared to the other models. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：Functional magnetic resonance imaging (fMRI) is widely used for clinical examinations, diagnosis, and treatment. By segmenting fMRI images, large-scale medical image data can be processed more efficiently. Most deep learning (DL)-based segmentation typically uses some type of encoding-decoding model. In this study, affective computing (AC) was developed using the brain fMRI dataset generated from an emotion simulation experiment. The brain fMRI dataset was segmented using an attention model, a deep convolutional neural network-32 (DCNN-32) based on Laplacian of Gaussian (LoG) filter, called ADCNN-32-G. For the evaluation of image segmentation, several indices are presented. By comparing the proposed ADCNN-32s-G model to distance regularized level set evolution (DRLSE), single-seeded region growing, and the single segNet full convolutional network model (FCN), the proposed model performs well in segmenting mass fMRI datasets. The proposed method can be applied to the real-time monitoring of patients with depression, and it can effectively advise human mental health. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：A key challenge in anomaly detection is the imbalance between the amounts of normal and abnormal signal data. Specifically, the amount of abnormal signal data is considerably less than that of normal signal data. To solve this problem, techniques for detecting abnormalities using only normal signal data derived from artificial immune systems (AISs) have been investigated. A representative example is the negative selection algorithm (NSA), which classifies data and detects anomalies using only normal signals through a process that mimics the underlying principle of vertebrate immunity. However, the NSA is optimized to detect only two classes of anomalies. Therefore, in this study, we developed a multiclass anomaly detection algorithm that hybridizes the principles of NSA and the clonal selection algorithm (CSA). We improved this algorithm using unsupervised and semi-supervised learning algorithms to conveniently detect anomalies at actual industrial sites. This paper presents a process for applying an AIS algorithm to anomaly detection using the evolution of data-based anomaly-detection algorithms. In particular, we leveraged the NSA principle of classification through semi-supervised learning to enable multiple classifications of unlabeled data. The obtained detector data formed clones optimized by the CSA and had constant memory, thus improving the classification accuracy and reducing run time. The proposed algorithm was validated using an intelligent maintenance system bearing dataset and a vacuum deposition equipment dataset.(C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：Rhinitis is a kind of respiratory disease that is difficult to cure. Timely and accurate prediction in its early stage is an effective method for diagnosis of rhinitis. Machine learning is often applied in predicting clinical rhinitis. However, those problems like multi-label features, class imbalance, and poor generalization performance usually occur on rhinitis prediction. This paper introduces an ensemble neural network chain model with pre-training on rhinitis multi-label classification. We apply stacked autoencoders for denoising and feature dimensionality reduction, add pre-training networks to extract global correlations, and build neural network chain to extract local relevant information for single-label classification. This proposed model can use both global and local label correlations to reduce the influence of unreasonable label sequences on classification. A total of 2231 clinical rhinitis cases from Shanghai Tongji Hospital affiliated to Tongji University is conducted for training and test. The cross-validation results show that the average Hamming Loss, accuracy, recall and F1-score is 0.0195, 87.88%, 92.32% and 92.88%, respectively. Compared to various typical multi-label classifiers, the proposed model achieves better generalization performance in evaluation measures. In addition, we calculate the feature importance of rhinitis based on the purity of splitting nodes in Random Forest and study the correlations between rhinitis features and classification, which have a good reference value for diagnosis and treatment of clinical rhinitis. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：N4-methylcytosine (4mC) has a significant effect on altering protein interactions, DNA conformation, gene expression and genomic imprinting. Accurate recognition of the 4mC sites is helpful for indepth study of biomedical research. Although there are experimental methods for detecting 4mC sites, these techniques are time-consuming and laborious, and cannot be applied to large-scale genome scanning. Therefore, supplementation with an efficient computational method is absolutely necessary. In this study, we propose a prediction tool, 4mCPred-FSVM, to solve the above problems. We use position-specific trinucleotide propensity (PSTNP) to construct feature vectors. Subsequently, the feature vector was used as the input of the fuzzy support vector machine (FSVM) and the final predictor was developed. We measure the performance of the model on six datasets. In comparison to the state-of-the-art predictor, our predictor has achieved much higher accuracies in predicting 4mC sites. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
Nimmi, K.Janet, B.Selvan, A. KalaiSivakumaran, N....
12页
查看更多>>摘要：The COVID-19 precautions, lockdown, and quarantine implemented throughout the epidemic resulted in a worldwide economic disaster. People are facing unprecedented levels of intense threat, necessitating professional, systematic psychiatric intervention and assistance. New psychological services must be established as quickly as possible to support the mental healthcare needs of people in this pandemic condition. This study examines the contents of calls landed in the emergency response support system (ERSS) during the pandemic. Furthermore, a combined analysis of Twitter patterns connected to emergency services could be valuable in assisting people in this pandemic crisis and understanding and supporting people's emotions. The proposed Average Voting Ensemble Deep Learning model (AVEDL Model) is based on the Average Voting technique. The AVEDL Model is utilized to classify emotion based on COVID-19 associated emergency response support system calls (transcribed) along with tweets. Pre-trained transformer-based models BERT, Disti1BERT, and RoBERTa are combined to build the AVEDL Model, which achieves the best results. The AVEDL Model is trained and tested for emotion detection using the COVID-19 labeled tweets and call content of the emergency response support system. This is the first deep learning ensemble model using COVID-19 emotion analysis to the best of our knowledge. The AVEDL Model outperforms standard deep learning and machine learning models by attaining an accuracy of 86.46 percent and Macro-average F1-score of 85.20 percent. (C) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：We consider the constrained longest common subsequence problem with an arbitrary set of input strings as well as an arbitrary set of pattern strings. This problem has applications, for example, in computational biology where it serves as a measure of similarity for sets of molecules with putative structures in common. We contribute in several ways. First, it is formally proven that finding a feasible solution of arbitrary length is, in general, NP-complete. Second, we propose several heuristic approaches: a greedy algorithm, a beam search aiming for feasibility, a variable neighborhood search, and a hybrid of the latter two approaches. An exhaustive experimental study shows the effectivity and differences of the proposed approaches in respect to finding a feasible solution, finding high-quality solutions, and runtime for both, artificial and real-world instance sets. The latter ones are generated from a set of 12681 bacteria 16S rRNA gene sequences and consider 15 primer contigs as pattern strings. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
NSTL
Elsevier