Nada Mohammed Murad¹, Adnan Yousif Dawod², Saadaldeen Rashid Ahmed^3,4,*, Ravi Sekhar⁵, Pritesh Shah⁵

Intelligent Automation & Soft Computing, Vol.40, pp. 1-27, 2025, DOI:10.32604/iasc.2024.058624 - 10 January 2025

Abstract This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity, focusing on network intrusion detection systems (NIDS). The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing a more flexible and robust security architecture. We use seven unique machine learning models to improve detection skills, emphasizing data quality, traceability, and transparency, facilitated by a blockchain layer that safeguards against data modification and ensures auditability. Our technique employs the Synthetic Minority Oversampling Technique (SMOTE) to equilibrate the dataset, therefore mitigating prevalent class imbalance difficulties… More >

Sarvenaz Sadat Khatami¹, Mehrdad Shoeibi², Anita Ershadi Oskouei³, Diego Martín^4,*, Maral Keramat Dashliboroun⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 881-911, 2025, DOI:10.32604/cmc.2024.059999 - 03 January 2025

Abstract The Internet of Things (IoT) is integral to modern infrastructure, enabling connectivity among a wide range of devices from home automation to industrial control systems. With the exponential increase in data generated by these interconnected devices, robust anomaly detection mechanisms are essential. Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns. This paper presents a novel approach utilizing generative adversarial networks (GANs) for anomaly detection in IoT systems. However, optimizing GANs involves tuning hyper-parameters such as learning rate, batch size, and optimization algorithms,… More >

Nahed Alsaleh^1,2, Reem Alnanih^1,*, Nahed Alowidi¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 949-976, 2025, DOI:10.32604/cmc.2024.059351 - 03 January 2025

Abstract App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products. Automating the analysis of these reviews is vital for efficient review management. While traditional machine learning (ML) models rely on basic word-based feature extraction, deep learning (DL) methods, enhanced with advanced word embeddings, have shown superior performance. This research introduces a novel aspect-based sentiment analysis (ABSA) framework to classify app reviews based on key non-functional requirements, focusing on usability factors: effectiveness, efficiency, and satisfaction. We propose a hybrid DL model, combining BERT (Bidirectional Encoder Representations from Transformers) More >

Mehmet Karahan^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 777-798, 2025, DOI:10.32604/cmc.2024.059123 - 03 January 2025

Abstract Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles (UAVs). Nowadays, people actively use quadrotor UAVs in essential missions such as search and rescue, counter-terrorism, firefighting, surveillance, and cargo transportation. While performing these tasks, quadrotors must operate in noisy environments. Therefore, a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance. Many researchers have focused only on white Gaussian noise in their studies, whereas researchers need to consider the effects of all colored noises during the operation of… More >

Jinlong Wang^1,2,*, Zhenyu Liu¹, Xingtao Yang¹, Min Li¹, Zhihan Lyu³

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1-39, 2025, DOI:10.32604/cmc.2024.058926 - 03 January 2025

Abstract With the rapid development of artificial intelligence, the Internet of Things (IoT) can deploy various machine learning algorithms for network and application management. In the IoT environment, many sensors and devices generate massive data, but data security and privacy protection have become a serious challenge. Federated learning (FL) can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing. This review aims to deeply explore the combination of FL and the IoT, and analyze the application of federated learning in the IoT from More >

Gabriela Dobrița, Simona-Vasilica Oprea^*, Adela Bâra

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 675-712, 2025, DOI:10.32604/cmc.2024.058656 - 03 January 2025

Abstract In the rapidly evolving landscape of television advertising, optimizing ad schedules to maximize viewer engagement and revenue has become significant. Traditional methods often operate in silos, limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing. This article introduces a novel approach that leverages Federated Learning (FL) to enhance TV ad schedule optimization, combining the strengths of local optimization techniques with the power of global Machine Learning (ML) models to uncover actionable insights without compromising data privacy. It combines linear programming for initial ads scheduling optimization with ML—specifically,… More >

Syed Tasnimul Karim Ayon^1,#, Farhan Md. Siraj^1,#, Jia Uddin^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 499-520, 2025, DOI:10.32604/cmc.2025.058361 - 03 January 2025

Abstract This study investigates the application of Learnable Memory Vision Transformers (LMViT) for detecting metal surface flaws, comparing their performance with traditional CNNs, specifically ResNet18 and ResNet50, as well as other transformer-based models including Token to Token ViT, ViT without memory, and Parallel ViT. Leveraging a widely-used steel surface defect dataset, the research applies data augmentation and t-distributed stochastic neighbor embedding (t-SNE) to enhance feature extraction and understanding. These techniques mitigated overfitting, stabilized training, and improved generalization capabilities. The LMViT model achieved a test accuracy of 97.22%, significantly outperforming ResNet18 (88.89%) and ResNet50 (88.90%), as well… More >

Amjad Rehman¹, Muhammad Mujahid¹, Alex Elyassih¹, Bayan AlGhofaily¹, Saeed Ali Omer Bahaj^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 41-72, 2025, DOI:10.32604/cmc.2024.058036 - 03 January 2025

Abstract In computer vision and artificial intelligence, automatic facial expression-based emotion identification of humans has become a popular research and industry problem. Recent demonstrations and applications in several fields, including computer games, smart homes, expression analysis, gesture recognition, surveillance films, depression therapy, patient monitoring, anxiety, and others, have brought attention to its significant academic and commercial importance. This study emphasizes research that has only employed facial images for face expression recognition (FER), because facial expressions are a basic way that people communicate meaning to each other. The immense achievement of deep learning has resulted in a… More >

Zhiguo Liu^1,#, Yuqing Gui^1,#, Lin Wang^2,*, Yingru Jiang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 863-879, 2025, DOI:10.32604/cmc.2024.057353 - 03 January 2025

Abstract Satellite edge computing has garnered significant attention from researchers; however, processing a large volume of tasks within multi-node satellite networks still poses considerable challenges. The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers, making it necessary to implement effective task offloading scheduling to enhance user experience. In this paper, we propose a priority-based task scheduling strategy based on a Software-Defined Network (SDN) framework for satellite-terrestrial integrated networks, which clarifies the execution order of tasks based on their priority. Subsequently, we More >

Khalil Ullah^1,*, Song Jian¹, Muhammad Naeem Ul Hassan¹, Suliman Khan², Mohammad Babar^3,*, Arshad Ahmad⁴, Shafiq Ahmad⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1151-1169, 2025, DOI:10.32604/cmc.2024.057328 - 03 January 2025

Abstract The emergence of next generation networks (NextG), including 5G and beyond, is reshaping the technological landscape of cellular and mobile networks. These networks are sufficiently scaled to interconnect billions of users and devices. Researchers in academia and industry are focusing on technological advancements to achieve high-speed transmission, cell planning, and latency reduction to facilitate emerging applications such as virtual reality, the metaverse, smart cities, smart health, and autonomous vehicles. NextG continuously improves its network functionality to support these applications. Multiple input multiple output (MIMO) technology offers spectral efficiency, dependability, and overall performance in conjunction with More >