Salahudin Robo^1,2, Triyanna Widiyaningtyas^1,*, Wahyu Sakti Gunawan Irianto¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.073011 - 09 December 2025

Abstract Recommendation systems are an integral and indispensable part of every digital platform, as they can suggest content or items to users based on their respective needs. Collaborative filtering is a technique often used in various studies, which produces recommendations by analyzing similarities between users and items based on their behavior. Although often used, traditional collaborative filtering techniques still face the main challenge of sparsity. Sparsity problems occur when the data in the system is sparse, meaning that only a portion of users provide feedback on some items, resulting in inaccurate recommendations generated by the system.… More >

Shitong Ye¹, Bo Yang^2,*, Hao Quan³, Shan Liu⁴, Minyi Tang⁵, Jiawei Tian^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2287-2306, 2025, DOI:10.32604/cmes.2025.066195 - 31 August 2025

Abstract Computer-aided surgical navigation technology helps and guides doctors to complete the operation smoothly, which simulates the whole surgical environment with computer technology, and then visualizes the whole operation link in three dimensions. At present, common image-guided surgical techniques such as computed tomography (CT) and X-ray imaging (X-ray) will cause radiation damage to the human body during the imaging process. To address this, we propose a novel Extended Kalman filter-based model that tracks the puncture needle-point using an ultrasound probe. To address the limitations of Kalman filtering methods based on position and velocity, our method of More >

Nurfazira Mohamed Fadil¹, Kelvin Kang Wee Tang^1,2,*, Malavige Don Eranda Kanchana Gunathilaka³, Abdullah Hisam Omar^1,2, Muhammad Fahim Supian¹

Revue Internationale de Géomatique, Vol.34, pp. 569-585, 2025, DOI:10.32604/rig.2025.066200 - 06 August 2025

Abstract Single-beam echo sounders remain popular for seabed mapping because they possess an affordable cost and user-friendly design, delivering essential services for marine navigation, coastal management and resource conservation. High-amplitude echoes known as sea-spikes can severely harm depth measurement precision by disrupting readings, thus lowering the overall data accuracy. The manual processing method for outliers produces subjective results and demands excessive labor, which makes it difficult to accomplish trustworthy data processing. The study presents the Sea-Spike Filtering System (SSFS) as a semi-automatic system that utilizes mean absolute deviation (MAD) together with median filter (MF) techniques to… More > Graphic Abstract

Ravi Nahta¹, Nagaraj Naik^2,*, Srivinay³, Swetha Parvatha Reddy Chandrasekhara⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 461-487, 2025, DOI:10.32604/cmes.2025.063973 - 31 July 2025

Abstract The exponential growth of over-the-top (OTT) entertainment has fueled a surge in content consumption across diverse formats, especially in regional Indian languages. With the Indian film industry producing over 1500 films annually in more than 20 languages, personalized recommendations are essential to highlight relevant content. To overcome the limitations of traditional recommender systems—such as static latent vectors, poor handling of cold-start scenarios, and the absence of uncertainty modeling—we propose a deep Collaborative Neural Generative Embedding (C-NGE) model. C-NGE dynamically learns user and item representations by integrating rating information and metadata features in a unified neural More >

Hyeong-Gyeong Kim¹, Sang-Min Choi², Hyeon Seo², Suwon Lee^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4381-4397, 2025, DOI:10.32604/cmc.2025.067024 - 30 July 2025

Abstract Adversarial attacks pose a significant threat to artificial intelligence systems by exposing them to vulnerabilities in deep learning models. Existing defense mechanisms often suffer drawbacks, such as the need for model retraining, significant inference time overhead, and limited effectiveness against specific attack types. Achieving perfect defense against adversarial attacks remains elusive, emphasizing the importance of mitigation strategies. In this study, we propose a defense mechanism that applies random cropping and Gaussian filtering to input images to mitigate the impact of adversarial attacks. First, the image was randomly cropped to vary its dimensions and then placed… More >

Jie Shao¹, Zihao Zhang¹, Quan Xu¹, Tiantian Cai¹, Junye Li¹, Baicheng Xiang¹, Shijie Li¹, Xianfeng Xu^2,*

Energy Engineering, Vol.122, No.6, pp. 2291-2307, 2025, DOI:10.32604/ee.2025.063302 - 29 May 2025

Abstract In response to the complex working conditions of the power grid caused by the high proportion of new energy access, which leads to insufficient output accuracy of the second-order generalized integrator (SOGI) phase-locked loop, this article proposes an improved frequency adaptive phase-locked loop structure for SOGI. Firstly, an amplitude compensation branch is introduced to compensate for the SOGI tracking fundamental frequency signal, ensuring the accuracy of the SOGI output orthogonal signal under frequency fluctuation conditions. Secondly, by cascading two adaptive SOGI modules, the suppression capability of low-order harmonics and Direct Current (DC) components has been More >

Yong Zheng^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4315-4328, 2025, DOI:10.32604/cmc.2025.063498 - 19 May 2025

Abstract A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors. Collaborative filtering, a popular technique within recommender systems, predicts user interests by analyzing patterns in interactions and similarities between users, leveraging past behavior data to make personalized recommendations. Despite its popularity, collaborative filtering faces notable challenges, and one of them is the issue of grey-sheep users who have unusual tastes in the system. Surprisingly, existing research has not extensively explored outlier detection techniques to address the grey-sheep problem. To fill this research gap, this study conducts… More >

Dang Hung Tran, Van Tinh Nguyen^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4679-4698, 2025, DOI:10.32604/cmc.2025.063228 - 19 May 2025

Abstract It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it… More >

Sony Peng¹, Sophort Siet¹, Ilkhomjon Sadriddinov¹, Dae-Young Kim^2,*, Kyuwon Park^3,*, Doo-Soon Park²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2041-2057, 2025, DOI:10.32604/cmc.2025.061166 - 16 April 2025

Abstract Recommendation systems (RSs) are crucial in personalizing user experiences in digital environments by suggesting relevant content or items. Collaborative filtering (CF) is a widely used personalization technique that leverages user-item interactions to generate recommendations. However, it struggles with challenges like the cold-start problem, scalability issues, and data sparsity. To address these limitations, we develop a Graph Convolutional Networks (GCNs) model that captures the complex network of interactions between users and items, identifying subtle patterns that traditional methods may overlook. We integrate this GCNs model into a federated learning (FL) framework, enabling the model to learn… More >

Haitao Wang^1,2, Yuanzhao Guo^1,2, Xiaotong Han^1,2, Yuan Tian^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 137-155, 2025, DOI:10.32604/cmc.2025.060534 - 26 March 2025

Abstract Relation extraction plays a crucial role in numerous downstream tasks. Dialogue relation extraction focuses on identifying relations between two arguments within a given dialogue. To tackle the problem of low information density in dialogues, methods based on trigger enhancement have been proposed, yielding positive results. However, trigger enhancement faces challenges, which cause suboptimal model performance. First, the proportion of annotated triggers is low in DialogRE. Second, feature representations of triggers and arguments often contain conflicting information. In this paper, we propose a novel Multi-Feature Filtering and Fusion trigger enhancement approach to overcome these limitations. We first… More >