Zhigang Zhao¹, Xiong Luo^1,2,3,*, Maojian Chen^1,2,3, Ling Ma¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513

Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation… More >

Shuqin Zhang, Xinyu Su^*, Yunfei Han, Tianhui Du, Peiyu Shi

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3993-4023, 2023, DOI:10.32604/cmc.2023.044526

Abstract Advanced Persistent Threat (APT) is now the most common network assault. However, the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks. They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats. To address the above problems, firstly, this paper constructs the multi-source threat element analysis ontology (MTEAO) by integrating multi-source network security knowledge bases. Subsequently, based on MTEAO, we propose a two-layer threat prediction model (TL-TPM) that combines the knowledge graph and the… More >

Ahmad F Subahi^*, Areej Athama

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3801-3816, 2023, DOI:10.32604/cmc.2023.034522

Abstract With the rapid growth in the availability of digital health-related data, there is a great demand for the utilization of intelligent information systems within the healthcare sector. These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks. They can also provide various sustainable health services such as medical error reduction, diagnosis acceleration, and clinical services quality improvement. The intensive care unit (ICU) is one of the most important hospital units. However, there are limited rooms and resources in most hospitals. During times of seasonal diseases and pandemics, ICUs face high admission demand. In… More >

Lingyu Liang¹, Xiangyu Zhao^1,*, Wenqi Huang¹, Liming Sun^2,3, Ziyao Wang³, Yaosen Zhan²

Energy Engineering, Vol.120, No.12, pp. 2839-2856, 2023, DOI:10.32604/ee.2023.042042

Abstract The N-1 criterion is a critical factor for ensuring the reliable and resilient operation of electric power distribution networks. However, the increasing complexity of distribution networks and the associated growth in data size have created a significant challenge for distribution network planners. To address this issue, we propose a fast N-1 verification procedure for urban distribution networks that combines CIM file data analysis with MILP-based mathematical modeling. Our proposed method leverages the principles of CIM file analysis for distribution network N-1 analysis. We develop a mathematical model of distribution networks based on CIM data and transfer it into MILP. We… More >

Xinzheng Wang^1,2,*, Bing Guo¹, Yan Shen³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1183-1206, 2024, DOI:10.32604/cmes.2023.029552

Abstract Predicting students’ academic achievements is an essential issue in education, which can benefit many stakeholders, for instance, students, teachers, managers, etc. Compared with online courses such as MOOCs, students’ academic-related data in the face-to-face physical teaching environment is usually sparsity, and the sample size is relatively small. It makes building models to predict students’ performance accurately in such an environment even more challenging. This paper proposes a Two-Way Neural Network (TWNN) model based on the bidirectional recurrent neural network and graph neural network to predict students’ next semester’s course performance using only their previous course achievements. Extensive experiments on a… More > Graphic Abstract

Yixia Chen^1,2, Mingwei Lin^1,2,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 845-866, 2023, DOI:10.32604/cmc.2023.040970

Abstract The consensus scheme is an essential component in the real blockchain environment. The Delegated Proof of Stake (DPoS) is a competitive consensus scheme that can decrease energy costs, promote decentralization, and increase efficiency, respectively. However, how to study the knowledge representation of the collective voting information and then select delegates is a new open problem. To ensure the fairness and effectiveness of transactions in the blockchain, in this paper, we propose a novel fine-grained knowledge representation method, which improves the DPoS scheme based on the linguistic term set (LTS) and proportional hesitant fuzzy linguistic term set (PHFLTS). To this end,… More >

Shuqin Zhang, Xinyu Su^*, Peiyu Shi, Tianhui Du, Yunfei Han

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 349-377, 2023, DOI:10.32604/cmc.2023.040964

Abstract Cyber Threat Intelligence (CTI) is a valuable resource for cybersecurity defense, but it also poses challenges due to its multi-source and heterogeneous nature. Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly. To address these challenges, we propose a novel approach that consists of three steps. First, we construct the attack and defense analysis of the cybersecurity ontology (ADACO) model by integrating multiple cybersecurity databases. Second, we develop the threat evolution prediction algorithm (TEPA), which can automatically detect threats at device nodes, correlate and map multi-source threat information,… More >

Yuyuan Xiang, Yangli Jia^*, Xiangliang Zhang, Zhenling Zhang

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 97-114, 2023, DOI:10.32604/cmc.2023.040751

Abstract Event extraction stands as a significant endeavor within the realm of information extraction, aspiring to automatically extract structured event information from vast volumes of unstructured text. Extracting event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the data. Although researchers have proposed various methods to accomplish this task, most existing event extraction models cannot address these challenges because they are only applicable to text scenarios. To solve the above issues, this paper proposes a multi-modal event extraction method based on knowledge fusion. Specifically, for event-type… More >

Jianguo Wang¹, Kuan Zhang^1,*, Yuesheng Zhao^2,*, Xiaoling Wang², Muhammad Shamrooz Aslam²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 79-96, 2023, DOI:10.32604/cmc.2023.040506

Abstract The purpose of Human Activities Recognition (HAR) is to recognize human activities with sensors like accelerometers and gyroscopes. The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms. In this paper, we experimentally validate the HAR process and its various algorithms independently. On the base of which, it is further proposed that, in addition to the necessary eigenvalues and intelligent algorithms, correct prior knowledge is even more critical. The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object, the sampling process, the sampling data, the HAR algorithm,… More >

Fiaz Majeed¹, Afzaal Ahmad¹, Muhammad Awais Hassan², Muhammad Shafiq^3,*, Jin-Ghoo Choi³, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 601-614, 2023, DOI:10.32604/cmc.2023.036074

Abstract Every day, the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis. Crime news exists on the Internet in unstructured formats such as books, websites, documents, and journals. From such homogeneous data, it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies. Keyword-based Information Retrieval (IR) systems rely on statistics to retrieve results, making it difficult to obtain relevant results. They are unable to understand the user's query and thus face word mismatches due to context changes… More >