Liang Mu¹, Yurui Kang¹, Zixu Yan¹, Guangyu Zhu^2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2495-2512, 2025, DOI:10.32604/cmc.2025.063208 - 03 July 2025

Abstract The accurate prediction and analysis of emergencies in Urban Rail Transit Systems (URTS) are essential for the development of effective early warning and prevention mechanisms. This study presents an integrated perception model designed to predict emergencies and analyze their causes based on historical unstructured emergency data. To address issues related to data structuredness and missing values, we employed label encoding and an Elastic Net Regularization-based Generative Adversarial Interpolation Network (ER-GAIN) for data structuring and imputation. Additionally, to mitigate the impact of imbalanced data on the predictive performance of emergencies, we introduced an Adaptive Boosting Ensemble… More >

Noor Ayesha¹, Muhammad Mujahid², Abeer Rashad Mirdad², Faten S. Alamri^3,*, Amjad R. Khan²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1883-1899, 2025, DOI:10.32604/cmc.2025.063560 - 09 June 2025

Abstract Large language models (LLMs) and natural language processing (NLP) have significant promise to improve efficiency and refine healthcare decision-making and clinical results. Numerous domains, including healthcare, are rapidly adopting LLMs for the classification of biomedical textual data in medical research. The LLM can derive insights from intricate, extensive, unstructured training data. Variants need to be accurately identified and classified to advance genetic research, provide individualized treatment, and assist physicians in making better choices. However, the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize. Such an… More >

Jingcheng Yang¹, Lili Fan², Hongmei Liu^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4725-4740, 2025, DOI:10.32604/cmc.2025.062963 - 19 May 2025

Abstract With the advancement of deep learning in the automotive domain, more and more researchers are focusing on autonomous driving. Among these tasks, free space detection is particularly crucial. Currently, many model-based approaches have achieved autonomous driving on well-structured urban roads, but these efforts primarily focus on urban road environments. In contrast, there are fewer deep learning methods specifically designed for off-road traversable area detection, and their effectiveness is not yet satisfactory. This is because detecting traversable areas in complex outdoor environments poses significant challenges, and current methods often rely on single-image inputs, which do not… More >

Md Hasibur Rahman, Mohammed Arif Uddin, Zinnat Fowzia Ria, Rashedur M. Rahman^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1637-1666, 2025, DOI:10.32604/cmes.2024.058329 - 27 January 2025

Abstract The rapid growth of digital data necessitates advanced natural language processing (NLP) models like BERT (Bidirectional Encoder Representations from Transformers), known for its superior performance in text classification. However, BERT’s size and computational demands limit its practicality, especially in resource-constrained settings. This research compresses the BERT base model for Bengali emotion classification through knowledge distillation (KD), pruning, and quantization techniques. Despite Bengali being the sixth most spoken language globally, NLP research in this area is limited. Our approach addresses this gap by creating an efficient BERT-based model for Bengali text. We have explored 20 combinations… More > Graphic Abstract

Mark Ariel Levin^*

Journal of Quantum Computing, Vol.6, pp. 15-24, 2024, DOI:10.32604/jqc.2024.047423 - 24 April 2024

Abstract Quantum algorithms for unstructured search problems rely on the preparation of a uniform superposition, traditionally achieved through Hadamard gates. However, this incidentally creates an auxiliary search space consisting of nonsensical answers that do not belong in the search space and reduce the efficiency of the algorithm due to the need to neglect, un-compute, or destructively interfere with them. Previous approaches to removing this auxiliary search space yielded large circuit depth and required the use of ancillary qubits. We have developed an optimized general solver for a circuit that prepares a uniform superposition of any N More >

Chengjun Wang^1,2, Fan Ding^2,*, Yiwen Wang¹, Renyuan Wu¹, Xingyu Yao², Chengjie Jiang¹, Liuyi Ling¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1481-1501, 2024, DOI:10.32604/cmc.2023.046876 - 30 January 2024

Abstract The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots. Real-time identification of strawberries in an unstructured environment is a challenging task. Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy. To this end, the present study proposes an Efficient YOLACT (E-YOLACT) algorithm for strawberry detection and segmentation based on the YOLACT framework. The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism, pyramid squeeze shuffle attention (PSSA), for efficient feature extraction. Additionally, an attention-guided… More >

Qingsong Fan, Qijie Rao, Haisong Huang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1825-1848, 2023, DOI:10.32604/cmes.2023.028369 - 26 June 2023

Abstract In present-day industrial settings, where robot arms perform tasks in an unstructured environment, there may exist numerous objects of various shapes scattered in random positions, making it challenging for a robot arm to precisely attain the ideal pose to grasp the object. To solve this problem, a multistage robotic arm flexible grasp detection method based on deep learning is proposed. This method first improves the Faster RCNN target detection model, which significantly improves the detection ability of the model for multiscale grasped objects in unstructured scenes. Then, a Squeeze-and-Excitation module is introduced to design a… More >

Amir Yasseen Mahdi^1,2,*, Siti Sophiayati Yuhaniz¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1375-1392, 2023, DOI:10.32604/cmc.2023.032671 - 22 September 2022

Abstract The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease. Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems. In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease. Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS). However, TF-IDF has several developments to improve its drawbacks, in particular, it is not… More >

Zubair Nabi¹, Ramzan Talib^1,*, Muhammad Kashif Hanif¹, Muhammad Awais²

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1357-1374, 2022, DOI:10.32604/csse.2022.025712 - 09 May 2022

Abstract Digitalization has changed the way of information processing, and new techniques of legal data processing are evolving. Text mining helps to analyze and search different court cases available in the form of digital text documents to extract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reduced time. The rapid development of judicial ontologies seems to deliver interesting problem solving to legal knowledge formalization. Mining context information through ontologies from corpora is a challenging and interesting field. This research paper presents More >

Kamal ElDahshan¹, Eman Selim², Ahmed Ismail Ebada², Mohamed Abouhawwash^3,4, Yunyoung Nam^5,*, Gamal Behery²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5149-5164, 2022, DOI:10.32604/cmc.2022.028326 - 21 April 2022

Abstract Currently, relational database management systems (RDBMSs) face different challenges in application development due to the massive growth of unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, which do not adhere to the relational model. The migration from relational databases to NoSQL databases is challenging due to the data complexity. This study aims to enhance the storage performance of RDBMSs in handling a variety of data. The paper presents two approaches. The first approach proposes a convenient representation of unstructured data storage. Several extensive experiments were More >