Hamayun Khan^1,*, Muhammad Atif Imtiaz², Hira Siddique³, Muhammad Tausif Afzal Rana⁴, Arshad Ali⁵, Muhammad Zeeshan Baig⁶, Saif ur Rehman⁷, Yazed Alsaawy⁵

Computer Systems Science and Engineering, Vol.49, pp. 317-331, 2025, DOI:10.32604/csse.2025.061118 - 19 March 2025

Abstract The migration of tasks aided by machine learning (ML) predictions IN (DPM) is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor. In this paper, we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling (EA-EDF). ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system. The proposed system model allocates processors… More >

Nataliya Shakhovska, Volodymyr Shymanskyi^*, Maksym Prymachenko

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4469-4484, 2025, DOI:10.32604/cmc.2025.062675 - 06 March 2025

Abstract Time series forecasting is important in the fields of finance, energy, and meteorology, but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data. In this paper, we propose the FractalNet-LSTM model, which combines fractal convolutional units with recurrent long short-term memory (LSTM) layers to model time series efficiently. To test the effectiveness of the model, data with complex structures and patterns, in particular, with seasonal and cyclical effects, were used. To better demonstrate the obtained results and the formed conclusions, the model performance was shown on the datasets More >

Abdul Ahad^1,2, Ira Puspitasari^1,3,*, Jiangbin Zheng², Shamsher Ullah⁴, Farhan Ullah⁵, Sheikh Tahir Bakhsh⁶, Ivan Miguel Pires^7,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

Woongkyu Park¹, Yeongyu Choi², Mahammad Shareef Mekala³, Gyu Sang Choi¹, Kook-Yeol Yoo¹, Ho-youl Jung^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3965-3981, 2025, DOI:10.32604/cmc.2025.059966 - 06 March 2025

Abstract Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In… More >

Can Hu¹, Shanqing Zhang^2,*, Kewei Tao², Gaoming Yang¹, Li Li²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4913-4930, 2025, DOI:10.32604/cmc.2025.059770 - 06 March 2025

Abstract The surge of large-scale models in recent years has led to breakthroughs in numerous fields, but it has also introduced higher computational costs and more complex network architectures. These increasingly large and intricate networks pose challenges for deployment and execution while also exacerbating the issue of network over-parameterization. To address this issue, various network compression techniques have been developed, such as network pruning. A typical pruning algorithm follows a three-step pipeline involving training, pruning, and retraining. Existing methods often directly set the pruned filters to zero during retraining, significantly reducing the parameter space. However, this… More >

Hao Li^1,#, Kuan Shao^1,#, Xin Wang², Mufeng Wang³, Zhenyong Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5387-5405, 2025, DOI:10.32604/cmc.2025.059718 - 06 March 2025

Abstract Due to the development of cloud computing and machine learning, users can upload their data to the cloud for machine learning model training. However, dishonest clouds may infer user data, resulting in user data leakage. Previous schemes have achieved secure outsourced computing, but they suffer from low computational accuracy, difficult-to-handle heterogeneous distribution of data from multiple sources, and high computational cost, which result in extremely poor user experience and expensive cloud computing costs. To address the above problems, we propose a multi-precision, multi-sourced, and multi-key outsourcing neural network training scheme. Firstly, we design a multi-precision More >

Amel Ksibi^1,*, Mohammed Zakariah², Latifah Almuqren¹, Ala Saleh Alluhaidan¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4093-4116, 2025, DOI:10.32604/cmc.2025.059615 - 06 March 2025

Abstract The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches, such as signature-based detection, are no longer effective due to the continuously advancing level of sophistication. To resolve this problem, efficient and flexible malware detection tools are needed. This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations. Moreover, the dataset used in this study is the CIC-AndMal2017, which contains 20,000 instances of network traffic across five distinct malware categories: a.… 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 >

Xinrong Zhang¹, Bo Chang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5079-5095, 2025, DOI:10.32604/cmc.2025.059469 - 06 March 2025

Abstract In the RSSI-based positioning algorithm, regarding the problem of a great conflict between precision and cost, a low-power and low-cost synergic localization algorithm is proposed, where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness. In the ranging period, the power attenuation factor is obtained through the wireless channel modeling, and the RSSI value is transformed into distance. In the positioning period, the preferred reference nodes are used to calculate coordinates. In the position optimization period, Taylor… More >

Haydar Abdulameer Marhoon^1,2,*, Rafid Sagban^3,4, Atheer Y. Oudah^1,5, Saadaldeen Rashid Ahmed^6,7

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4181-4218, 2025, DOI:10.32604/cmc.2025.058822 - 06 March 2025

Abstract In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, healthcare monitoring, and environmental surveillance increasingly deploy WSNs, recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity. The proposed method innovatively partitions the network into logical segments or virtual barriers, allowing for targeted monitoring and data collection that aligns with specific traffic patterns. This approach not only improves the diversit. There are more types of data in the training set,… More >