Shaya A. Alshaya^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3075-3095, 2025, DOI:10.32604/cmc.2025.062360 - 16 April 2025

Abstract The integration of visual elements, such as emojis, into educational content represents a promising approach to enhancing student engagement and comprehension. However, existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation. This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes (DECOC), Long Short-Term Memory (LSTM) networks, and Multi-Layer Deep Neural Networks (ML-DNN) to identify optimal emoji placements within computer science course materials. The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings… More >

Amjad A. Alsuwaylimi^1,*, Zaid S. Alenezi²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3165-3185, 2025, DOI:10.32604/cmc.2025.061674 - 16 April 2025

Abstract Cyberbullying is a remarkable issue in the Arabic-speaking world, affecting children, organizations, and businesses. Various efforts have been made to combat this problem through proposed models using machine learning (ML) and deep learning (DL) approaches utilizing natural language processing (NLP) methods and by proposing relevant datasets. However, most of these endeavors focused predominantly on the English language, leaving a substantial gap in addressing Arabic cyberbullying. Given the complexities of the Arabic language, transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained… More >

Jiahao Wu^1,2, Jinzhong Xu¹, Xiaoming Liu^1,*, Guan Yang^1,3, Jie Liu⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2809-2828, 2025, DOI:10.32604/cmc.2025.061359 - 16 April 2025

Abstract Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain, due to the entity bias arising from the variation of entity information between different domains, which makes large language models prone to spurious correlations problems when dealing with specific domains and entities. In order to solve this problem, this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement, which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing… More >

Pratik Jadhav¹, Vuppala Adithya Sairam¹, Niranjan Bhojane¹, Abhyuday Singh¹, Shilpa Gite^1,2, Biswajeet Pradhan^3,*, Mrinal Bachute¹, Abdullah Alamri⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3493-3517, 2025, DOI:10.32604/cmc.2025.060764 - 16 April 2025

Abstract Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time real-time. Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems. The low-cost thermal imaging software produces low-resolution thermal images in grayscale format, hence necessitating methods for improving the resolution and colorizing the images. The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images, followed by a sparse autoencoder for colorization of thermal images and a multimodal convolutional neural network for… More >

Hamza Murad Khan¹, Anwar Khan^1,*, Santos Gracia Villar^2,3,4, Luis Alonso Dzul Lopez^2,5,6, Abdulaziz Almaleh⁷, Abdullah M. Al-Qahtani⁸

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3369-3388, 2025, DOI:10.32604/cmc.2025.060474 - 16 April 2025

Abstract Traffic forecasting with high precision aids Intelligent Transport Systems (ITS) in formulating and optimizing traffic management strategies. The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity. To address this problem, this paper uses the Tree-structured Parzen Estimator (TPE) to tune the hyperparameters of the Long Short-term Memory (LSTM) deep learning framework. The Tree-structured Parzen Estimator (TPE) uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples. This ensures fast convergence in… More >

Bushra Tayyaba¹, Muhammad Usman Ghani Khan^1,2,3, Talha Waheed², Shaha Al-Otaibi⁴, Tanzila Saba^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2851-2864, 2025, DOI:10.32604/cmc.2025.060365 - 16 April 2025

Abstract Reference Evapotranspiration (ETo) is widely used to assess total water loss between land and atmosphere due to its importance in maintaining the atmospheric water balance, especially in agricultural and environmental management. Accurate estimation of ETo is challenging due to its dependency on multiple climatic variables, including temperature, humidity, and solar radiation, making it a complex multivariate time-series problem. Traditional machine learning and deep learning models have been applied to forecast ETo, achieving moderate success. However, the introduction of transformer-based architectures in time-series forecasting has opened new possibilities for more precise ETo predictions. In this study,… More >

Mohammed Abdelmajeed^1,*, Jiangbin Zheng¹, Ahmed Murtadha¹, Youcef Nafa¹, Mohammed Abaker², Muhammad Pervez Akhter³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3471-3491, 2025, DOI:10.32604/cmc.2025.059870 - 16 April 2025

Abstract Arabic Dialect Identification (DID) is a task in Natural Language Processing (NLP) that involves determining the dialect of a given piece of text in Arabic. The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect. Despite the effectiveness of these solutions, the performance heavily relies on the amount of labeled examples, which is labor-intensive to attain and may not be readily available in real-world scenarios. To alleviate the burden of labeling data, this paper introduces a novel solution that leverages… More >

Rana Muhammad Adnan^1,2, Mo Wang^1,*, Adil Masood³, Ozgur Kisi^4,5,6,*, Shamsuddin Shahid⁷, Mohammad Zounemat-Kermani⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1249-1272, 2025, DOI:10.32604/cmes.2025.062339 - 11 April 2025

Abstract Accurate daily suspended sediment load (SSL) prediction is essential for sustainable water resource management, sediment control, and environmental planning. However, SSL prediction is highly complex due to its nonlinear and dynamic nature, making traditional empirical models inadequate. This study proposes a novel hybrid approach, integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Gradient-Based Optimizer (GBO), to enhance SSL forecasting accuracy. The research compares the performance of ANFIS-GBO with three alternative models: standard ANFIS, ANFIS with Particle Swarm Optimization (ANFIS-PSO), and ANFIS with Grey Wolf Optimization (ANFIS-GWO). Historical SSL and streamflow data from the Bailong… More >

Alex Kibet^*, Gilbert Langat

Journal of Intelligent Medicine and Healthcare, Vol.3, pp. 1-14, 2025, DOI:10.32604/jimh.2022.062824 - 04 April 2025

Abstract Multiple Sclerosis (MS) is a disease that disrupts the flow of information within the brain. It affects approximately 1 million people in the US. And remains incurable. MS treatments can cause side effects and impact the quality of life and even survival rates. Based on existing research studies, we investigate the risks and benefits of three treatment options based on methylprednisolone (a corticosteroid hormone medication) prescribed in (1) high-dose, (2) low-dose, or (3) no treatment. The study currently prescribes one treatment to all patients as it has been proven to be the most effective on More >

Josua Käser¹, Thomas Nagy¹, Patrick Stirnemann¹, Thomas Hanne^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 201-217, 2025, DOI:10.32604/cmc.2025.061527 - 26 March 2025

Abstract We analyze the suitability of existing pre-trained transformer-based language models (PLMs) for abstractive text summarization on German technical healthcare texts. The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field. The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts, even if the model is not specifically trained in that language. Through experiments, the research questions explore the performance of transformer language models in dealing with complex syntax constructs, the difference… More >