Ying Su¹, Morgan C. Wang¹, Shuai Liu^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189

Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing memory cells and gating mechanisms… More >

S. Jayanthi^*, S. Prasanna Devi

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2137-2158, 2024, DOI:10.32604/cmc.2024.045975

Abstract In healthcare, the persistent challenge of arrhythmias, a leading cause of global mortality, has sparked extensive research into the automation of detection using machine learning (ML) algorithms. However, traditional ML and AutoML approaches have revealed their limitations, notably regarding feature generalization and automation efficiency. This glaring research gap has motivated the development of AutoRhythmAI, an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias. Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection, effectively bridging the gap between data preprocessing and model selection. To validate our system, we have rigorously… More >

Anıl Sezgin^1,2,*, Aytuğ Boyacı³

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2121-2143, 2023, DOI:10.32604/cmc.2023.040287

Abstract By identifying and responding to any malicious behavior that could endanger the system, the Intrusion Detection System (IDS) is crucial for preserving the security of the Industrial Internet of Things (IIoT) network. The benefit of anomaly-based IDS is that they are able to recognize zero-day attacks due to the fact that they do not rely on a signature database to identify abnormal activity. In order to improve control over datasets and the process, this study proposes using an automated machine learning (AutoML) technique to automate the machine learning processes for IDS. Our ground-breaking architecture, known as AID4I, makes use of… More >

Fadwa Alrowais¹, Sami Althahabi², Saud S. Alotaibi³, Abdullah Mohamed⁴, Manar Ahmed Hamza^5,*, Radwa Marzouk⁶

Computer Systems Science and Engineering, Vol.45, No.1, pp. 687-700, 2023, DOI:10.32604/csse.2023.030188

Abstract Recently, Internet of Things (IoT) devices produces massive quantity of data from distinct sources that get transmitted over public networks. Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved. The development of automated tools for cyber threat detection and classification using machine learning (ML) and artificial intelligence (AI) tools become essential to accomplish security in the IoT environment. It is needed to minimize security issues related to IoT gadgets effectively. Therefore, this article introduces a new Mayfly optimization (MFO) with regularized extreme learning machine (RELM) model, named MFO-RELM for Cybersecurity… More >

Jian Liu¹, Yipeng Du¹, Xiang Wang^1,*, Wuguang Yue², Jim Feng³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1995-2011, 2022, DOI:10.32604/cmc.2022.029073

Abstract Epilepsy is a common neurological disease and severely affects the daily life of patients. The automatic detection and diagnosis system of epilepsy based on electroencephalogram (EEG) is of great significance to help patients with epilepsy return to normal life. With the development of deep learning technology and the increase in the amount of EEG data, the performance of deep learning based automatic detection algorithm for epilepsy EEG has gradually surpassed the traditional hand-crafted approaches. However, the neural architecture design for epilepsy EEG analysis is time-consuming and laborious, and the designed structure is difficult to adapt to the changing EEG collection… More >

Jeongcheol Lee, Sunil Ahn^*, Hyunseob Kim, Jongsuk Ruth Lee

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 255-277, 2022, DOI:10.32604/iasc.2022.018558

Abstract Automated hyperparameter optimization (HPO) is a crucial and time-consuming part in the automatic generation of efficient machine learning models. Previous studies could be classified into two major categories in terms of reducing training overhead: (1) sampling a promising hyperparameter configuration and (2) pruning non-promising configurations. These adaptive sampling and resource scheduling are combined to reduce cost, increasing the number of evaluations done on more promising configurations to find the best model in a given time. That is, these strategies are preferred to identify the best-performing models at an early stage within a certain deadline. Although these time and resource constraints… More >