Jiachang Liu^1,*, Zhengwei Huang², Junfeng Xiang¹, Lu Liu¹, Manlin Hu¹

Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

Tajim Md. Niamat Ullah Akhund^1,2,*, Waleed M. Al-Nuwaiser³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222 - 12 September 2024

Abstract This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While More >

Abdulrahman Alzahrani^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2331-2349, 2024, DOI:10.32604/cmc.2024.052804 - 15 August 2024

Abstract The recent development of the Internet of Things (IoTs) resulted in the growth of IoT-based DDoS attacks. The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices. Anomaly detection models evaluate transmission patterns, network traffic, and device behaviour to detect deviations from usual activities. Machine learning (ML) techniques detect patterns signalling botnet activity, namely sudden traffic increase, unusual command and control patterns, or irregular device behaviour. In addition, intrusion detection systems (IDSs) and signature-based techniques are applied to recognize known malware signatures related to botnets.… More >

Zhenyu Qian¹, Yizhang Jiang¹, Zhou Hong¹, Lijun Huang², Fengda Li³, KhinWee Lai⁶, Kaijian Xia^4,5,6,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4741-4762, 2024, DOI:10.32604/cmc.2024.050920 - 20 June 2024

Abstract In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction… More > Graphic Abstract

Duy Quang Tran¹, Huy Q. Tran^2,*, Minh Van Nguyen³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760 - 26 March 2024

Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal… More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963 - 27 February 2024

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for… More >

Sumaya S. Sulaiman^1,2,*, Ibraheem Nadher³, Sarab M. Hameed²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1049-1069, 2024, DOI:10.32604/cmc.2023.046051 - 30 January 2024

Abstract Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network… More >

Ibrahim M. Alwayle¹, Hala J. Alshahrani², Saud S. Alotaibi³, Khaled M. Alalayah¹, Amira Sayed A. Aziz⁴, Khadija M. Alaidarous¹, Ibrahim Abdulrab Ahmed⁵, Manar Ahmed Hamza^6,*

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 153-168, 2023, DOI:10.32604/iasc.2023.034718 - 05 February 2024

Abstract Abstractive text summarization is crucial to produce summaries of natural language with basic concepts from large text documents. Despite the achievement of English language-related abstractive text summarization models, the models that support Arabic language text summarization are fewer in number. Recent abstractive Arabic summarization models encounter different issues that need to be resolved. Syntax inconsistency is a crucial issue resulting in the low-accuracy summary. A new technique has achieved remarkable outcomes by adding topic awareness in the text summarization process that guides the module by imitating human awareness. The current research article presents Abstractive Arabic… More >

Hafza Ayesha Siddiqa¹, Muhammad Irfan¹, Saadullah Farooq Abbasi^2,*, Wei Chen¹

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1759-1778, 2023, DOI:10.32604/cmc.2023.041970 - 29 November 2023

Abstract Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual… More >

Rufus Gikera^1,*, Jonathan Mwaura², Elizaphan Muuro³, Shadrack Mambo³

Journal on Artificial Intelligence, Vol.5, pp. 75-112, 2023, DOI:10.32604/jai.2023.043229 - 26 October 2023

Abstract k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this… More >