Igor Musikhin^*

Revue Internationale de Géomatique, Vol.34, pp. 939-957, 2025, DOI:10.32604/rig.2025.071168 - 09 December 2025

Abstract Urban environmental quality research is crucial, as cities become competitive centers concentrating human talent, industrial activity, and financial resources, contributing significantly to national economies. Municipal and government priorities include retaining residents, preventing skilled worker outflow, and meeting the evolving needs of urban populations. The study presents the development and application of a scenario-based spatial analysis tool for assessing urban environmental quality at a detailed spatial scale within the city of Novosibirsk. Using advanced geoinformatics, GIS techniques, and an expert knowledge base, the tool integrates diverse thematic data layers with user-defined scenarios to compute and visualize… More >

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2071-2094, 2025, DOI:10.32604/cmc.2025.068533 - 29 August 2025

Abstract Smart learning environments have been considered as vital sources and essential needs in modern digital education systems. With the rapid proliferation of smart and assistive technologies, smart learning processes have become quite convenient, comfortable, and financially affordable. This shift has led to the emergence of pervasive computing environments, where user’s intelligent behavior is supported by smart gadgets; however, it is becoming more challenging due to inconsistent behavior of Artificial intelligence (AI) assistive technologies in terms of networking issues, slow user responses to technologies and limited computational resources. This paper presents a context-aware predictive reasoning based… More >

Yinuo Chen¹, Xu Wu¹, Jiaxin Fan¹, Guangyu Zhu^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1597-1613, 2025, DOI:10.32604/cmc.2025.062779 - 09 June 2025

Abstract The consultation intention of emergency decision-makers in urban rail transit (URT) is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services. This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions. However, the current structured degree of the URT emergency knowledge base remains low, and the domain questions lack labeled datasets, resulting in a large deviation between the consultation outcomes and the intended objectives. To address this issue, this paper proposes a question intention recognition model for the URT emergency domain,… More >

Muhammad Hameed Siddiqi^1,*, Menwa Alshammeri¹, Jawad Khan^2,*, Muhammad Faheem Khan³, Asfandyar Khan⁴, Madallah Alruwaili¹, Yousef Alhwaiti¹, Saad Alanazi¹, Irshad Ahmad⁵

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5345-5371, 2025, DOI:10.32604/cmc.2025.062340 - 19 May 2025

Abstract As legal cases grow in complexity and volume worldwide, integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus. This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain. The proposed framework comprises three core modules: legal feature extraction, semantic similarity assessment, and verdict recommendation. For legal feature extraction, a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts. Semantic similarity between cases is evaluated using a hybrid method that… More >

Xianju Gao^*, Huachun Zhou, Weilin Wang, Jingfu Yan

Computer Systems Science and Engineering, Vol.49, pp. 19-48, 2025, DOI:10.32604/csse.2024.058327 - 03 January 2025

Abstract The network security knowledge base standardizes and integrates network security data, providing a reliable foundation for real-time network security protection solutions. However, current research on network security knowledge bases mainly focuses on their construction, while the potential to optimize intelligent security services for real-time network security protection requires further exploration. Therefore, how to effectively utilize the vast amount of historical knowledge in the field of network security and establish a feedback mechanism to update it in real time, thereby enhancing the detection capability of security services against malicious traffic, has become an important issue. Our… More >

Xiaohui Cui^1,2,#, Chao Song^1,2,#, Dongmei Li^1,2,*, Xiaolong Qu^1,2, Jiao Long^1,2, Yu Yang^1,2, Hanchao Zhang³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3603-3618, 2024, DOI:10.32604/cmc.2024.047321 - 26 March 2024

Abstract Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer… More >

Ouyang Liu, Kun Li^*, Ziwei Yin, Deyun Gao, Huachun Zhou

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2955-2977, 2023, DOI:10.32604/iasc.2023.039995 - 11 September 2023

Abstract Due to the many types of distributed denial-of-service attacks (DDoS) attacks and the large amount of data generated, it becomes a challenge to manage and apply the malicious behavior knowledge generated by DDoS attacks. We propose a malicious behavior knowledge base framework for DDoS attacks, which completes the construction and application of a multi-domain malicious behavior knowledge base. First, we collected malicious behavior traffic generated by five mainstream DDoS attacks. At the same time, we completed the knowledge collection mechanism through data pre-processing and dataset design. Then, we designed a malicious behavior category graph and… More >

Thi Thanh Sang Nguyen^*, Dang Huu Trong Ho, Ngoc Tram Anh Nguyen

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 601-616, 2023, DOI:10.32604/iasc.2023.032080 - 29 September 2022

Abstract Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in many fields. However, these systems depend on learning methods and training data. Therefore, it is necessary to prepare such a good dataset, but it is not an easy job. An ontology-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions. This study proposes a novel chatbot model involving ontology to generate efficient responses automatically. A case study of admissions advising at the International University–VNU HCMC is taken into account… More >

Hai Long¹, Zhe Wang¹, Yidi Cui^2,3, Junhui Wang⁴, Bo Gao⁵, Chao Chen⁵, Yan Zhu^5,*, Heinrich Herre¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5197-5217, 2022, DOI:10.32604/cmc.2022.029365 - 28 July 2022

Abstract Psoriasis is a chronic, non-communicable, painful, disfiguring and disabling disease for which there is no cure, with great negative impact on patients’ quality of life (QoL). Diagnosis and treatment with traditional Chinese medical technique based on syndrome differentiation has been used in practice for a long time and proven effective, though, up to now, there are only a few available studies about the use of semantic technologies and the knowledge systems that use Traditional Chinese Medicine (TCM)-syndrome differentiation for information retrieval and automated reasoning. In this paper we use semantic techniques based on ontologies to… More >

Prachi Agrawal¹, Khalid Alnowibet², Ali Wagdy Mohamed^3,4,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2847-2868, 2022, DOI:10.32604/cmc.2022.023126 - 07 December 2021

Abstract This paper presents a novel application of metaheuristic algorithms for solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithm is based on human behavior in which people gain and share their knowledge with others. Different types of stochastic fractional programming problems are considered in this study. The augmented Lagrangian method (ALM) is used to handle these constrained optimization problems by converting them into unconstrained optimization problems. Three examples from the literature are considered and transformed into their deterministic form using the chance-constrained technique. The transformed problems are More >