Huaruo Chen^1,2, Ya Wen^3,*, Fei Liu^4,5, Yihong Zhao⁶, Zhiyao Xie⁷, Xuehui Zhang⁴

International Journal of Mental Health Promotion, Vol.25, No.2, pp. 153-172, 2023, DOI:10.32604/ijmhp.2022.025546

Abstract Career identity is one of the important topics to study the psychological problems of employees, especially after the outbreak of CoV-19. Being a populous country in the world, China has a large number of employees from all walks of life, and its research on career identity has attracted more and more attention. Thus, this study aims to sort out the history, characteristics, and trends of the research on career identity in China through a literature review, which can help more scholars pay attention to China’s career identity research. In this study, Citespace is used to statistically analyze the selected literature… More > Graphic Abstract

Bowen Sun¹, Yamin Li^1,2,3,*, Jun Zhang¹, Honghong Xu¹, Xiaoqiang Ma⁴, Ping Xia^2,3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 157-176, 2023, DOI:10.32604/cmes.2023.025082

Abstract Generation-based linguistic steganography is a popular research area of information hiding. The text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention to. However, in the course of our experiment, we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text, which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases, and that the topic of generated texts is uncontrollable, so there is still room for improvement in concealment. In this paper,… More >

Hanan Abdullah Mengash¹, Hamed Alqahtani², Mohammed Maray³, Mohamed K. Nour⁴, Radwa Marzouk¹, Mohammed Abdullah Al-Hagery⁵, Heba Mohsen⁶, Mesfer Al Duhayyim^7,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5251-5265, 2023, DOI:10.32604/cmc.2023.032729

Abstract Autism Spectrum Disorder (ASD) refers to a neuro-disorder where an individual has long-lasting effects on communication and interaction with others. Advanced information technology which employs artificial intelligence (AI) model has assisted in early identify ASD by using pattern detection. Recent advances of AI models assist in the automated identification and classification of ASD, which helps to reduce the severity of the disease. This study introduces an automated ASD classification using owl search algorithm with machine learning (ASDC-OSAML) model. The proposed ASDC-OSAML model majorly focuses on the identification and classification of ASD. To attain this, the presented ASDC-OSAML model follows min-max… More >

Zefeng Gu, Hua Chen^*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2497-2514, 2023, DOI:10.32604/cmes.2023.024332

Abstract Link prediction, also known as Knowledge Graph Completion (KGC), is the common task in Knowledge Graphs (KGs) to predict missing connections between entities. Most existing methods focus on designing shallow, scalable models, which have less expressive than deep, multi-layer models. Furthermore, most operations like addition, matrix multiplications or factorization are handcrafted based on a few known relation patterns in several well-known datasets, such as FB15k, WN18, etc. However, due to the diversity and complex nature of real-world data distribution, it is inherently difficult to preset all latent patterns. To address this issue, we propose KGE-ANS, a novel knowledge graph embedding… More >

Jin Wang¹, Wei Ou¹, Osama Alfarraj², Amr Tolba², Gwang-Jun Kim^3,*, Yongjun Ren⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1805-1819, 2023, DOI:10.32604/csse.2023.029622

Abstract Since transactions in blockchain are based on public ledger verification, this raises security concerns about privacy protection. And it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification, when the whole transaction on the chain is verified. In order to improve the efficiency and privacy protection of block data verification, this paper proposes an efficient block verification mechanism with privacy protection based on zero-knowledge proof (ZKP), which not only protects the privacy of users but also improves the speed of data block verification. There is no need to put the whole… More >

Siti Zulaikha Mohd Jamaludin¹, Mohd. Asyraf Mansor², Aslina Baharum^3,*, Mohd Shareduwan Mohd Kasihmuddin¹, Habibah A. Wahab⁴, Muhammad Fadhil Marsani¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2853-2870, 2023, DOI:10.32604/cmc.2023.032654

Abstract The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data. Specifically, the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions. The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy. To address the issues, the logical permutation serves as an alternative mechanism that can enhance the… More >

Byeongmin Choi¹, YongHyun Lee¹, Yeunwoong Kyung², Eunchan Kim^3,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 71-82, 2023, DOI:10.32604/iasc.2023.032783

Abstract Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA). However, there is a problem that the models do not directly use explicit information of knowledge sources existing outside. To augment this, additional methods such as knowledge-aware graph network (KagNet) and multi-hop graph relation network (MHGRN) have been proposed. In this study, we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers (ALBERT) with knowledge graph information extraction technique. We also propose to applying the novel method, schema graph expansion to recent… 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

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 in the proposed chatbot. A… More >

Ahmed Abdulhakim Al-Absi¹, Dae-Ki Kang^2,*, Mohammed Abdulhakim Al-Absi³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 671-691, 2023, DOI:10.32604/cmc.2023.030262

Abstract Sentiment analysis (AS) is one of the basic research directions in natural language processing (NLP), it is widely adopted for news, product review, and politics. Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity of a given target context, previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy. Hence this research work develops a deep-semantic and contextual knowledge networks (DSCNet). DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects. At first temporal relationships are established then… More >

Le Quang Thao^1,2,*, Duong Duc Cuong², Vu Manh Hung³, Le Thanh Vinh³, Doan Trong Nghia⁴, Dinh Ha Hai³, Nguyen Nhan Nhi³

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3517-3530, 2023, DOI:10.32604/iasc.2023.031026

Abstract The recent outbreak of the coronavirus disease of 2019 (Covid-19) has been causing many disruptions among the education systems worldwide, most of them due to the abrupt transition to online learning. The sudden upsurge in digital electronic devices usage, namely personal computers, laptops, tablets and smartphones is unprecedented, which leads to a new wave of both mental and physical health problems among students, for example eye-related illnesses. The overexposure to electronic devices, extended screen time usage and lack of outdoor sunlight have put a consequential strain on the student’s ophthalmic health because of their young age and a relative lack… More >