Jing Yang, Bin Ji, Shasha Li^*, Jun Ma, Jie Yu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4303-4315, 2024, DOI:10.32604/cmc.2023.035594

Abstract Named entity recognition (NER) is a fundamental task of information extraction (IE), and it has attracted considerable research attention in recent years. The abundant annotated English NER datasets have significantly promoted the NER research in the English field. By contrast, much fewer efforts are made to the Chinese NER research, especially in the scientific domain, due to the scarcity of Chinese NER datasets. To alleviate this problem, we present a Chinese scientific NER dataset–SciCN, which contains entity annotations of titles and abstracts derived from 3,500 scientific papers. We manually annotate a total of 62,059 entities, and these entities are classified… More >

SHAN OU^#, YUN XU^#, QINGLAN LIU, TIANWEN YANG, WEI CHEN, XIU YUAN, XIN ZUO, PENG SHI^*, JIE YAO^*

BIOCELL, Vol.48, No.3, pp. 415-429, 2024, DOI:10.32604/biocell.2024.046178

Abstract Background: The interrelation between intestinal polyps, metabolic syndrome (MetS), and colorectal cancer (CRC) is a critical area of study. This research focuses on pinpointing potential molecular targets to understand the link between intestinal polyp formation, metabolic irregularities, and CRC progression. Methods: We examined clinical samples from patients with intestinal polyps coexisting with MetS and compared them with samples from patients with standard intestinal polyps. Transcriptome sequencing and public database analysis were employed to identify significant pathways and genes. These targets were then validated through immunohistochemistry (IHC). Following the RNA interference of key target expression, a series of experiments, including the… More > Graphic Abstract

Tomio Okawa^*,†

Frontiers in Heat and Mass Transfer, Vol.3, No.1, pp. 1-8, 2012, DOI:10.5098/hmt.v3.1.3001

Abstract The present article summarizes the studies on the thermalhydraulic condition under which the onset of nucleate boiling (ONB) is triggered in subcooled flow boiling. Available correlations and experimental data show that the ONB is tended to be delayed in mini and microchannels. It is known that the ONB condition is significantly dependent on the surface condition even in conventional-sized channels. Accumulation of ONB data accompanied by the information on the surface condition is therefore considered of importance to elucidate the mechanisms of boiling incipience in microchannels. Discussion is also made for the bubble dynamics observed in mini and microchannels. It… More >

Murugan Palanikumar¹, Nasreen Kausar^2,*, Dragan Pamucar^3,4, Seifedine Kadry^5,6,7,*, Chomyong Kim⁸, Yunyoung Nam⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3353-3385, 2024, DOI:10.32604/cmes.2024.031439

Abstract In this article, multiple attribute decision-making problems are solved using the vague normal set (VNS). It is possible to generalize the vague set (VS) and q-rung fuzzy set (FS) into the q-rung vague set (VS). A log q-rung normal vague weighted averaging (log q-rung NVWA), a log q-rung normal vague weighted geometric (log q-rung NVWG), a log generalized q-rung normal vague weighted averaging (log Gq-rung NVWA), and a log generalized q-rung normal vague weighted geometric (log Gq-rung NVWG) operator are discussed in this article. A description is provided of the scoring function, accuracy function and operational laws of the log… More >

Chirag Chandrashekar¹, K. P. Vijayakumar^1,*, K. Pradeep¹, A. Balasundaram^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.047697

Abstract The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength… More >

Thanh-Lam Nguyen¹, Hao Kao¹, Thanh-Tuan Nguyen², Mong-Fong Horng^1,*, Chin-Shiuh Shieh^1,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2181-2205, 2024, DOI:10.32604/cmc.2024.047387

Abstract Since its inception, the Internet has been rapidly evolving. With the advancement of science and technology and the explosive growth of the population, the demand for the Internet has been on the rise. Many applications in education, healthcare, entertainment, science, and more are being increasingly deployed based on the internet. Concurrently, malicious threats on the internet are on the rise as well. Distributed Denial of Service (DDoS) attacks are among the most common and dangerous threats on the internet today. The scale and complexity of DDoS attacks are constantly growing. Intrusion Detection Systems (IDS) have been deployed and have demonstrated… More >

Bayi Xu¹, Lei Sun^2,*, Xiuqing Mao², Chengwei Liu³, Zhiyi Ding²

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1995-2022, 2024, DOI:10.32604/cmc.2023.046478

Abstract In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models, we apply various preprocessing… More >

Emad Ul Haq Qazi^1,*, Tanveer Zia¹, Muhammad Imran², Muhammad Hamza Faheem¹

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 225-240, 2023, DOI:10.32604/iasc.2023.041181

Abstract Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified. In the current cyber world where deepfakes have shaken the global community, confirming the legitimacy of a digital image is of great importance. With the advancements made in deep learning techniques, now we can efficiently train and develop state-of-the-art digital image forensic models. The most traditional and widely used method by researchers is convolution neural networks (CNN) for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training.… More >

Kwok Tai Chui^1,*, Brij B. Gupta^2,3,4,5,6,*, Varsha Arya^7,8,9, Miguel Torres-Ruiz¹⁰

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1363-1379, 2024, DOI:10.32604/cmc.2023.046762

Abstract The visions of Industry 4.0 and 5.0 have reinforced the industrial environment. They have also made artificial intelligence incorporated as a major facilitator. Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure, and thus timely maintenance can ensure safe operations. Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model, which typically involves two datasets. In response to the availability of multiple datasets, this paper proposes using selective and adaptive incremental transfer learning (SA-ITL), which fuses three… More >

Xiaoge Wei^1,2,*, Yuming Zhang^1,2, Huaitao Song^1,2, Hengjie Qin^1,2, Guanjun Zhao³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1295-1316, 2024, DOI:10.32604/cmes.2023.045096

Abstract Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years. As part of this effort, an enhanced sparrow search algorithm (MSSA) was proposed. Firstly, the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm. Secondly, the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima. Finally, the local search mechanism based on the mountain climbing method was incorporated into the local search stage of… More >