Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Badraddin Alturki³, Ayman Yafoz¹, Ali Alqahtani⁴, Raed Alsini¹, Sami Saeed Binyamin⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1117-1140, 2025, DOI:10.32604/cmes.2025.070745 - 30 October 2025

Abstract QR codes are widely used in applications such as information sharing, advertising, and digital payments. However, their growing adoption has made them attractive targets for malicious activities, including malware distribution and phishing attacks. Traditional detection approaches rely on URL analysis or image-based feature extraction, which may introduce significant computational overhead and limit real-time applicability, and their performance often depends on the quality of extracted features. Previous studies in malicious detection do not fully focus on QR code security when combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs). This research proposes a deep learning… More >

Yahya Alhaj Maz¹, Mohammed Anbar¹, Selvakumar Manickam^1,*, Mosleh M. Abualhaj², Sultan Ahmed Almalki³, Basim Ahmad Alabsi⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5287-5307, 2025, DOI:10.32604/cmc.2025.068257 - 23 October 2025

Abstract The Internet of Things (IoT) is an innovation that combines imagined space with the actual world on a single platform. Because of the recent rapid rise of IoT devices, there has been a lack of standards, leading to a massive increase in unprotected devices connecting to networks. Consequently, cyberattacks on IoT are becoming more common, particularly keylogging attacks, which are often caused by security vulnerabilities on IoT networks. This research focuses on the role of transfer learning and ensemble classifiers in enhancing the detection of keylogging attacks within small, imbalanced IoT datasets. The authors propose… More >

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 >

Prasanalakshmi Balaji^1,2, Sangita Babu³, Maode Ma⁴, Zhaoxi Fang², Syarifah Bahiyah Rahayu^5,6,*, Mariyam Aysha Bivi¹, Mahaveerakannan Renganathan⁷

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5831-5858, 2025, DOI:10.32604/cmc.2025.065260 - 30 July 2025

Abstract The rapid progression of the Internet of Things (IoT) technology enables its application across various sectors. However, IoT devices typically acquire inadequate computing power and user interfaces, making them susceptible to security threats. One significant risk to cloud networks is Distributed Denial-of-Service (DoS) attacks, where attackers aim to overcome a target system with excessive data and requests. Among these, low-rate DoS (LR-DoS) attacks present a particular challenge to detection. By sending bursts of attacks at irregular intervals, LR-DoS significantly degrades the targeted system’s Quality of Service (QoS). The low-rate nature of these attacks confuses their… 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 >

Israa Ibraheem Al Barazanchi^1,2,*, Wahidah Hashim¹, Reema Thabit¹, Mashary Nawwaf Alrasheedy^3,4, Abeer Aljohan⁵, Jongwoon Park⁶, Byoungchol Chang⁶

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4787-4832, 2024, DOI:10.32604/cmc.2024.055079 - 19 December 2024

Abstract This research aims to enhance Clinical Decision Support Systems (CDSS) within Wireless Body Area Networks (WBANs) by leveraging advanced machine learning techniques. Specifically, we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) layers and echo state cells. These models are tailored to improve diagnostic precision, particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases. Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex, sequential medical data, struggling with long-term dependencies and data… More >