Lingyun Xiang¹, Hang Fu¹, Chunfang Yang^2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062834

Abstract In the Visual Place Recognition (VPR) task, existing research has leveraged large-scale pre-trained models to improve the performance of place recognition. However, when there are significant environmental differences between query images and reference images, a large number of ineffective local features will interfere with the extraction of key landmark features, leading to the retrieval of visually similar but geographically different images. To address this perceptual aliasing problem caused by environmental condition changes, we propose a novel Visual Place Recognition method with Cross-Environment Robust Feature Enhancement (CerfeVPR). This method uses the GAN network to generate similar… More >

Jun Wang^1,2, Chaoren Ge^1,2, Yihong Li^1,2, Huimin Zhao^1,2, Qiang Fu^1,2,*, Kerang Cao^1,2, Hoekyung Jung^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062094

Abstract Network Intrusion Detection System (NIDS) detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments. To improve the detection capability of minority-class attacks, this study proposes an intrusion detection method based on a two-layer structure. The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic, majority class attacks, and merged minority class attacks. The second layer further segments the minority class attacks through Stacking ensemble learning. The datasets are selected from the generic network dataset CIC-IDS2017, NSL-KDD, and the… More >

Prashanth B. S^1,*, Manoj Kumar M. V.^2,*, Nasser Almuraqab³, Puneetha B. H⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060881

Abstract Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models are built on the assumption of a static learning environment, but in practical situations, the data generated by the process is dynamic. This evolution of the data is termed concept drift. This research paper presents an approach for predicting mechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment. The method proposed here is applicable… More >

Nila Cecília Faria Lopes Medeiros^1,2, Gisele Amaral-Labat¹, Leonardo Iusuti de Medeiros^1,2, Alan Fernando Ney Boss¹, Beatriz Carvalho da Silva Fonseca¹, Manuella Gobbo de Castro Munhoz³, Guilherme F. B. Lenz e Silva³, Mauricio Ribeiro Baldan¹, Flavia Lega Braghiroli^4,*

Journal of Renewable Materials, DOI:10.32604/jrm.2025.02025-0017

Abstract Biochar, a carbon-rich material derived from the thermochemical conversion of biomass under oxygen-free conditions, has emerged as a sustainable resource for radar-absorbing technologies. This study explores the production of activated biochars from end-of-life wood panels using a scalable and sustainable physical activation method with CO₂ at different temperatures, avoiding the extensive use of corrosive chemicals and complex procedures associated with chemical or vacuum activation. Compared to conventional chemically or vacuum-activated biochars, the physically activated biochar demonstrated competitive performance while minimizing environmental impact, operational complexity, and energy consumption. Furthermore, activation at 750°C reduces energy consumption by 14%… More > Graphic Abstract

Walaa M. Hashim, Israa S. Ahmed, Ayad K. Khlief^*, Raed A. Jessam, Ameer Abed Jaddoa

Energy Engineering, DOI:10.32604/ee.2025.061630

Abstract Achieving broadband solar thermal absorption via dilute nanofluids is still a daunting challenge since the absorption peaks of common metal particles are usually located in the visible part of the radiation spectrum. This paper aims to present the results of experimental investigations on the thermal performance of heat pipe-type evacuated solar collectors. The experimented system consists of 15 tubes, providing the hot nanofluid to 100-L storage in a closed flow loop. The solar collector with a gross area of 2.1 m² is part of the solar hot water test system located in Baghdad-Iraq. Al₂O₃ nanofluid at… More >

Yi-Chen Lee¹, Yann-Horng Lin², Wen-Jer Chang^2,*, Muhammad Shamrooz Aslam^3,*, Zi-Yao Lin²

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.064717

Abstract An optimal fuzzy tracking synthesis for nonlinear discrete-time descriptor systems is discussed through the Parallel Distributed Compensation (PDC) approach and the Proportional-Difference (P-D) feedback framework. Based on the Takagi-Sugeno Fuzzy Descriptor Model (T-SFDM), a nonlinear discrete-time descriptor system is represented as several linear fuzzy subsystems, which facilitates the linear P-D feedback technique and streamlines the fuzzy controller design process. Leveraging the P-D feedback fuzzy controller, the closed-loop T-SFDM can be transformed into a standard system that guarantees non-impulsiveness and causality for the nonlinear discrete-time descriptor system. In view of the disturbance problems, a passive performance… More >

Muhammad Asif Zahoor Raja¹, Aqsa Zafar Abbasi², Kottakkaran Sooppy Nisar^3,*, Ayesha Rafiq², Muhammad Shoaib⁴

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.058020

Abstract Being a nonlinear operator, fractional derivatives can affect the enforcement of existence at any given time. As a result, the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations (FODEs). The goal of this study is to increase the fractional model of the TB virus’s (FMTBV) accuracy. Stochastic solvers have never been used to solve FMTBV previously. The Bayesian regularized artificial (BRA) method and neural networks (NNs), often referred to as BRA-NNs, were used to solve the FMTBV model. Each scenario features five occurrences that each reflect a different… More >

Yury V. Knyazev, Semyon T. Baidak, Yury I. Kuz’min, Alexey V. Lukoyanov^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.065091

Abstract The work presents the electronic structure computations and optical spectroscopy studies of half-Heusler ScNiBi and YNiBi compounds. Our first-principles computations of the electronic structures were based on density functional theory accounting for spin-orbit coupling. These compounds are computed to be semiconductors. The calculated gap values make ScNiBi and YNiBi valid for thermoelectric and optoelectronic applications and as selective filters. In ScNiBi and YNiBi, an intense peak at the energy of −2 eV is composed of the Ni 3d states in the conduction band, and the valence band mostly contains these states with some contributions from the… More >

Yuanyuan Ma¹, Cunzhi Hou¹, Ang Chen¹, Jinghui Zhang¹, Ruixia Jin², Ruixiang Li^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064523

Abstract Identifying cloud IP usage scenarios is critical for cybersecurity applications, yet existing machine learning methods rely heavily on numerous features, resulting in high complexity and low interpretability. To address these issues, this paper proposes an approach to identify cloud IPs from the perspective of network attributes. We employ data mining and crowdsourced collection strategies to gather IP addresses from various usage scenarios, which including cloud IPs and non-cloud IPs. On this basis, we establish a cloud IP identification feature set that includes attributes such as Autonomous System Number (ASN) and organization information. By analyzing the… More >

Arshiya Sajid Ansari^1,*, Ghadir Altuwaijri², Fahad Alodhyani¹, Moulay Ibrahim El-Khalil Ghembaza³, Shahabas Manakunnath Devasam Paramb³, Mohammad Sajid Mohammadi³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064027

Abstract IoT has emerged as a game-changing technology that connects numerous gadgets to networks for communication, processing, and real-time monitoring across diverse applications. Due to their heterogeneous nature and constrained resources, as well as the growing trend of using smart gadgets, there are privacy and security issues that are not adequately managed by conventional security measures. This review offers a thorough analysis of contemporary AI solutions designed to enhance security within IoT ecosystems. The intersection of AI technologies, including ML, and blockchain, with IoT privacy and security is systematically examined, focusing on their efficacy in addressing… More >