Zeyi Wang¹, Mingxi Jiao¹, Daliang Wang¹, Minxu Liu¹, Minglei Jiang², He Wang³, Shiqiang Li^3,*

Energy Engineering, Vol.121, No.8, pp. 2275-2291, 2024, DOI:10.32604/ee.2024.028223

Abstract This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method. Three different electrical quantities are selected as observations in the compressed sensing algorithm. The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels. Subsequently, by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,… More >

Sana Sahar Guia¹, Abdelkader Laouid¹, Mohammad Hammoudeh^2,*, Mostafa Kara^1,3

Computer Systems Science and Engineering, Vol.48, No.4, pp. 915-935, 2024, DOI:10.32604/csse.2024.046414

Abstract Artificial intelligence has been involved in several domains. Despite the advantages of using artificial intelligence techniques, some crucial limitations prevent them from being implemented in specific domains and locations. The accuracy, poor quality of gathered data, and processing time are considered major concerns in implementing machine learning techniques, certainly in low-end smart devices. This paper aims to introduce a novel pre-treatment technique dedicated to image text detection that uses the images’ pixel divergence and similarity to reduce the image size. Mitigating the image size while keeping its features improves the model training time with an… More >

Zhihui Song, Jinchen Xu, Kewei Li, Zheng Shan^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4573-4601, 2024, DOI:10.32604/cmc.2024.049310

Abstract Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations. However, due to limitations in code representation and neural network design, the validity and practicality of the model still need to be improved. Additionally, due to differences in programming languages, most methods lack cross-language detection generality. To address these issues, in this paper, we analyze the shortcomings of previous code representations and neural networks. We propose a novel hierarchical code representation that combines Concrete Syntax Trees (CST)… More >

Jing Zheng, Jianya Zhou, Yanping Zhu, Qian Shen, Jianying Zhou

Oncology Research, Vol.25, No.4, pp. 545-550, 2017, DOI:10.3727/096504016X14801968368898

Abstract Pancreatic metastasis from non-small cell lung cancer (NSCLC) is usually asymptomatic or presents with abdominal pain, acute pancreatitis, or jaundice. A lung primary is associated with worse survival compared to pancreatic metastases from other organs. Surgical treatment of solitary metastasis to the pancreas from NSCLC has been reviewed in several studies, one of which had a notable disease-free interval. To our knowledge, there are no prior reports of targeted therapy of pancreatic metastasis of NSCLC followed by a significant response. Herein we report the case of a 31-year-old female with a solitary pancreatic metastasis from ALK-rearranged More >

Chunhua Wang^1,2, Wenqian Shang^1,2,*, Tong Yi^3,*, Haibin Zhu⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1939-1956, 2024, DOI:10.32604/cmc.2024.048135

Abstract The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and More >

Hao Jiang¹, Yuerong Liao¹, Dongdong Zhao², Wenjian Luo³, Xingyi Zhang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1045-1075, 2024, DOI:10.32604/cmes.2024.048653

Abstract Due to the presence of a large amount of personal sensitive information in social networks, privacy preservation issues in social networks have attracted the attention of many scholars. Inspired by the self-nonself discrimination paradigm in the biological immune system, the negative representation of information indicates features such as simplicity and efficiency, which is very suitable for preserving social network privacy. Therefore, we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks, called AttNetNRI. Specifically, a negative survey-based method is developed to disturb the relationship between nodes in the… More >

Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT)… More >

Yingchao Li^1,*, Jianbin Wang¹, Haibin Wang²

Energy Engineering, Vol.121, No.4, pp. 1049-1065, 2024, DOI:10.32604/ee.2023.045228

Abstract With the increasing demand for electrical services, wind farm layout optimization has been one of the biggest challenges that we have to deal with. Despite the promising performance of the heuristic algorithm on the route network design problem, the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored. In this paper, the wind farm layout optimization problem is defined. Then, a multi-objective algorithm based on Graph Neural Network (GNN) and Variable Neighborhood Search (VNS) algorithm is proposed. GNN provides the basis representations for the following search algorithm so that the expressiveness… More >

Guilhem Paillard-Brunet^*, Audrey Couillet

Psycho-Oncologie, Vol.18, No.1, pp. 23-31, 2024, DOI:10.32604/po.2023.047888

Abstract Cette étude qualitative visait à recueillir chez les professionnels d’un Centre de Lutte Contre le Cancer les attentes et les réticences qu’ils pouvaient avoir vis-à-vis des interventions de pair-aidance. Des entretiens individuels semi-structurés ont été menés auprès de 12 professionnels issus de professions différentes. Une retranscription intégrale des entretiens puis une analyse thématique de leur contenu ont été conduites. L’analyse des données a permis de faire émerger trois thèmes principaux quant aux attentes exprimées par les soignants : le besoin d’un accompagnement plus soutenant des patients, rompre leur isolement dans la maladie et enrichir le More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for… More >