Hoang-Dieu Vu^1,2, Duc-Nghia Tran³, Quang-Tu Pham¹, Ngoc-Linh Nguyen^4,*, Duc-Tan Tran^1,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2819-2835, 2025, DOI:10.32604/cmc.2025.068355 - 23 September 2025

Abstract This study presents CGB-Net, a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer, with direct applicability to gastroesophageal reflux disease (GERD) monitoring. Unlike conventional approaches limited to four basic postures, CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions, providing enhanced resolution for personalized health assessment. The architecture introduces a unique integration of three complementary components: 1D Convolutional Neural Networks (1D-CNN) for efficient local spatial feature extraction, Gated Recurrent Units (GRU) to capture short-term temporal dependencies with reduced computational complexity, and Bidirectional Long Short-Term Memory… More >

Bandar Alotaibi^1,*, Aljawhara Almutarie², Shuaa Alotaibi³, Munif Alotaibi⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4451-4467, 2025, DOI:10.32604/cmc.2025.066050 - 30 July 2025

Abstract X (formerly known as Twitter) is one of the most prominent social media platforms, enabling users to share short messages (tweets) with the public or their followers. It serves various purposes, from real-time news dissemination and political discourse to trend spotting and consumer engagement. X has emerged as a key space for understanding shifting brand perceptions, consumer preferences, and product-related sentiment in the fashion industry. However, the platform’s informal, dynamic, and context-dependent language poses substantial challenges for sentiment analysis, mainly when attempting to detect sarcasm, slang, and nuanced emotional tones. This study introduces a hybrid… More >

Harendra Pratap Singh^1,2, Ishfaq Hussain Rather³, Sushil Kumar¹, Mohammad Aljaidi⁴, Omprakash Kaiwartya^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5577-5595, 2025, DOI:10.32604/cmc.2025.065741 - 30 July 2025

Abstract Electric Vehicles (EVs) have emerged as a cleaner, low-carbon, and environmentally friendly alternative to traditional internal combustion engine (ICE) vehicles. With the increasing adoption of EVs, they are expected to eventually replace ICE vehicles entirely. However, the rapid growth of EVs has significantly increased energy demand, posing challenges for power grids and infrastructure. This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road. To address these challenges, various deep… More >

Sahbi Boubaker^1,*, Adel Mellit^2,3,*, Nejib Ghazouani⁴, Walid Meskine⁵, Mohamed Benghanem⁶, Habib Kraiem^7,8

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2237-2259, 2025, DOI:10.32604/cmes.2025.064530 - 30 May 2025

Abstract Electric vehicles (EVs) are gradually being deployed in the transportation sector. Although they have a high impact on reducing greenhouse gas emissions, their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging. To cope with these problems, this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting. The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’ charging scheduling task. By using predictive algorithms for solar generation and load demand… More >

Wonbyung Lee, Jang Hyun Kim^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5393-5412, 2025, DOI:10.32604/cmc.2025.065413 - 19 May 2025

Abstract Predicting player performance in sports is a critical challenge with significant implications for team success, fan engagement, and financial outcomes. Although, in Major League Baseball (MLB), statistical methodologies such as sabermetrics have been widely used, the dynamic nature of sports makes accurate performance prediction a difficult task. Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions. This study addresses this challenge by employing the temporal fusion transformer (TFT), an advanced and cutting-edge deep learning model for complex data, to predict pitchers’ earned run average (ERA), a key More >

Nanjiang Zhong^*, Xinchen Jiang, Yuan Yao

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4741-4757, 2025, DOI:10.32604/cmc.2025.059536 - 06 March 2025

Abstract The proliferation of rumors on social media has caused serious harm to society. Although previous research has attempted to use deep learning methods for rumor detection, they did not simultaneously consider the two key features of temporal and spatial domains. More importantly, these methods struggle to automatically generate convincing explanations for the detection results, which is crucial for preventing the further spread of rumors. To address these limitations, this paper proposes a novel method that integrates both temporal and spatial features while leveraging Large Language Models (LLMs) to automatically generate explanations for the detection results.… More >

Navaneetha Krishnan Muthunambu¹, Senthil Prabakaran², Balasubramanian Prabhu Kavin³, Kishore Senthil Siruvangur⁴, Kavitha Chinnadurai¹, Jehad Ali^5,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172 - 26 March 2024

Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent… More >

Zhaoan Wang¹, Shaoping Xiao^1,*, Cheryl Reuben², Qiyu Wang², Jun Wang²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 285-297, 2023, DOI:10.32604/cmc.2023.044366 - 31 October 2023

Abstract This paper presents designing sequence-to-sequence recurrent neural network (RNN) architectures for a novel study to predict soil NOx emissions, driven by the imperative of understanding and mitigating environmental impact. The study utilizes data collected by the Environmental Protection Agency (EPA) to develop two distinct RNN predictive models: one built upon the long-short term memory (LSTM) and the other utilizing the gated recurrent unit (GRU). These models are fed with a combination of historical and anticipated air temperature, air moisture, and NOx emissions as inputs to forecast future NOx emissions. Both LSTM and GRU models can… More >

Loc Vu-Quoc^1,*, Alexander Humer²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130 - 26 June 2023

Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting… More >

Jin Wang^1,2, Yongsong Zou¹, Se-Jung Lim^3,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2743-2755, 2023, DOI:10.32604/iasc.2023.033869 - 15 March 2023

Abstract Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel… More >