B. Gomathi^1,*, R. Manimegalai¹, Srivatsan Santhanam², Atreya Biswas³

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1749-1765, 2024, DOI:10.32604/csse.2024.056659 - 22 November 2024

Abstract In enterprise operations, maintaining manual rules for enterprise processes can be expensive, time-consuming, and dependent on specialized domain knowledge in that enterprise domain. Recently, rule-generation has been automated in enterprises, particularly through Machine Learning, to streamline routine tasks. Typically, these machine models are black boxes where the reasons for the decisions are not always transparent, and the end users need to verify the model proposals as a part of the user acceptance testing to trust it. In such scenarios, rules excel over Machine Learning models as the end-users can verify the rules and have more… More >

Chandraumakantham Om Kumar^1,*, Sudhakaran Gajendran², Suguna Marappan¹, Mohammed Zakariah³, Abdulaziz S. Almazyad⁴

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 471-501, 2024, DOI:10.32604/cmc.2024.054966 - 15 October 2024

Abstract The security of the wireless sensor network-Internet of Things (WSN-IoT) network is more challenging due to its randomness and self-organized nature. Intrusion detection is one of the key methodologies utilized to ensure the security of the network. Conventional intrusion detection mechanisms have issues such as higher misclassification rates, increased model complexity, insignificant feature extraction, increased training time, increased run time complexity, computation overhead, failure to identify new attacks, increased energy consumption, and a variety of other factors that limit the performance of the intrusion system model. In this research a security framework for WSN-IoT, through… More >

Jiqiang Chen, Ranran Han, Dongqing Zhang, Litao Ma^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5067-5082, 2024, DOI:10.32604/cmc.2024.051494 - 20 June 2024

Abstract Imbalanced datasets are common in practical applications, and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes. However, the creation of fuzzy rules typically depends on expert knowledge, which may not fully leverage the label information in training data and may be subjective. To address this issue, a novel fuzzy rule oversampling approach is developed based on the learning vector quantization (LVQ) algorithm. In this method, the label information of the training data is utilized to determine the antecedent… More >

Hui Du, Zhiyuan Hu^*, Depeng Lu, Jingrui Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3239-3259, 2024, DOI:10.32604/cmc.2024.046314 - 15 May 2024

Abstract Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets with uneven density. Additionally, they incur substantial computational costs when applied to high-dimensional data due to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset and compute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similarity matrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a vote for the point with the highest density among its KNN. By utilizing the vote counts More >

Hongyan Xia, Jin Zhu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1637-1661, 2024, DOI:10.32604/cmes.2023.029576 - 17 November 2023

Abstract Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to cope with the development trend of large-scale ships. In order to improve the solution efficiency of the existing space-time network (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guided vehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balance constraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added to acquire the model STN_A. The coupling constraint in STN_A is updated, and… More >

Chien-Ming Chen¹, Zhenzhou Zhang¹, Jimmy Ming-Tai Wu¹, Kuruva Lakshmanna^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 733-759, 2023, DOI:10.32604/cmes.2023.027463 - 23 April 2023

Abstract Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. More >

Min Hu^1,2,3, Zhimin Chen⁴, Yuan Xia⁴, Liping Zhang^1,2,3,*, Qiuhua Tang^1,2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2815-2840, 2023, DOI:10.32604/cmes.2023.027146 - 09 March 2023

Abstract The multi-skill resource-constrained project scheduling problem (MS-RCPSP) is a significant management science problem that extends from the resource-constrained project scheduling problem (RCPSP) and is integrated with a real project and production environment. To solve MS-RCPSP, it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme. This paper proposes an improved gene expression programming (IGEP) approach to explore newly dispatching rules that can broadly solve MS-RCPSP. A new backward traversal decoding mechanism, and several neighborhood operators are applied in IGEP. The backward traversal decoding mechanism dramatically More > Graphic Abstract

Francisco José García-Peñalvo^*, Andrea Vázquez-Ingelmo, Alicia García-Holgado

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1023-1051, 2023, DOI:10.32604/cmes.2023.023897 - 06 February 2023

Abstract The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models’ outcomes could result in misleading conclusions that ultimately could become very dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every… More > Graphic Abstract

Mohsen Eid¹, Tamer Medhat^2,*, Manal E. Ali³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1531-1544, 2023, DOI:10.32604/csse.2023.031745 - 03 November 2022

Abstract An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a… More >

Ahmed Abdu Alattab^1,*, Mohammed Eid Ibrahim¹, Reyazur Rashid Irshad¹, Anwar Ali Yahya², Amin A. Al-Awady³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2397-2413, 2023, DOI:10.32604/cmc.2023.030924 - 31 October 2022

Abstract This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed More >