Idongesit Sunday Ambrose¹, Sunday Edet Etuk², Okechukwu Ebuka Agbasi^3,*, Ijah Ioryue Silas⁴, Unyime Udoette Saturday⁵, Eyo Edet Orok⁶

Revue Internationale de Géomatique, Vol.34, pp. 335-350, 2025, DOI:10.32604/rig.2025.064552 - 13 June 2025

Abstract Road construction and traffic congestion are increasingly recognized as major contributors to environmental and public health challenges in urban Nigeria, particularly in Rivers State. Despite growing urbanization, a gap remains in localized data on the combined effects of air and noise pollution in hydrocarbon-polluted environments. This study addresses that gap by conducting a preliminary environmental health assessment focused on the Port Harcourt Ring Road project. Air quality and noise levels were monitored in situ at 20 strategically selected locations, with five control points included for baseline comparison. Digital portable meters were used to measure concentrations of… More >

Canadian Journal of Urology, Vol.32, No.2, pp. 1767-1767, 2025

Abstract This article has no abstract. More >

Yu Zhou¹, Eneko Osaba², Xiao Zhang^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 237-242, 2025, DOI:10.32604/cmc.2025.066663 - 09 June 2025

Abstract This article has no abstract. More >

Haotong Wang^#,*, Liyan Wang^#, Yves Lepage

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 305-324, 2025, DOI:10.32604/cmc.2025.066351 - 09 June 2025

Abstract Data curation is vital for selecting effective demonstration examples in graph-to-text generation. However, evaluating the quality of Knowledge Graphs (KGs) remains challenging. Prior research exhibits a narrow focus on structural statistics, such as the shortest path length, while the correctness of graphs in representing the associated text is rarely explored. To address this gap, we introduce a dual-perspective evaluation framework for KG-text data, based on the computation of structural adequacy and semantic alignment. From a structural perspective, we propose the Weighted Incremental Edge Method (WIEM) to quantify graph completeness by leveraging agreement between relation models… More >

Xingsi Xue¹, Himanshu Dhumras^2,*, Garima Thakur³, Rakesh Kumar Bajaj⁴, Varun Shukla⁵

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 583-606, 2025, DOI:10.32604/cmc.2025.066310 - 09 June 2025

Abstract Ensuring a sustainable and eco-friendly environment is essential for promoting a healthy and balanced social life. However, decision-making in such contexts often involves handling vague, imprecise, and uncertain information. To address this challenge, this study presents a novel multi-criteria decision-making (MCDM) approach based on picture fuzzy hypersoft sets (PFHSS), integrating the flexibility of Schweizer-Sklar triangular norm-based aggregation operators. The proposed aggregation mechanisms—weighted average and weighted geometric operators—are formulated using newly defined operational laws under the PFHSS framework and are proven to satisfy essential mathematical properties, such as idempotency, monotonicity, and boundedness. The decision-making model systematically… More >

Seungmin Lee¹, Beomseong Kim², Heesung Lee^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1397-1415, 2025, DOI:10.32604/cmc.2025.066152 - 09 June 2025

Abstract This study proposes an advanced vision-based technology for detecting glass products and identifying defects in a smart glass factory production environment. Leveraging artificial intelligence (AI) and computer vision, the research aims to automate glass detection processes and maximize production efficiency. The primary focus is on developing a precise glass detection and quality management system tailored to smart manufacturing environments. The proposed system utilizes the various YOLO (You Only Look Once) models for glass detection, comparing their performance to identify the most effective architecture. Input images are preprocessed using a Gaussian Mixture Model (GMM) to remove… More >

Ibrahim T. Teke^1,2, Ahmet H. Ertas^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 243-264, 2025, DOI:10.32604/cmc.2025.066086 - 09 June 2025

Abstract This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting, runner system optimization, and structural analysis to significantly enhance the performance of injection-molded parts. At its core, the framework employs a greedy algorithm that generates runner systems based on adjacency and shortest path principles, leading to improvements in both mechanical strength and material efficiency. The design optimization is validated through a series of rigorous experimental tests, including three-point bending and torsion tests performed on key-socket frames, ensuring that the optimized designs meet practical performance requirements. A critical innovation of… More >

Mukesh Dalal^1,*, Payal Mittal²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 57-91, 2025, DOI:10.32604/cmc.2025.066056 - 09 June 2025

Abstract Deep learning-based object detection has revolutionized various fields, including agriculture. This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years, highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time. The review emphasizes the integration of advanced models, such as You Only Look Once (YOLO) v9, v10, EfficientDet, Transformer-based models, and hybrid frameworks that improve the precision, accuracy, and scalability for crop monitoring and More >

Ala’a R. Al-Shamasneh¹, Faten Khalid Karim², Arsalan Mahmoodzadeh^3,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 281-303, 2025, DOI:10.32604/cmc.2025.065748 - 09 June 2025

Abstract Soilcrete is a composite material of soil and cement that is highly valued in the construction industry. Accurate measurement of its mechanical properties is essential, but laboratory testing methods are expensive, time-consuming, and include inaccuracies. Machine learning (ML) algorithms provide a more efficient alternative for this purpose, so after assessment with a statistical extraction method, ML algorithms including back-propagation neural network (BPNN), K-nearest neighbor (KNN), radial basis function (RBF), feed-forward neural networks (FFNN), and support vector regression (SVR) for predicting the uniaxial compressive strength (UCS) of soilcrete, were proposed in this study. The developed models… More >

Indu Bala^*, Wathsala Karunarathne, Lewis Mitchell

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 727-744, 2025, DOI:10.32604/cmc.2025.065706 - 09 June 2025

Abstract Recent advancements in computational and database technologies have led to the exponential growth of large-scale medical datasets, significantly increasing data complexity and dimensionality in medical diagnostics. Efficient feature selection methods are critical for improving diagnostic accuracy, reducing computational costs, and enhancing the interpretability of predictive models. Particle Swarm Optimization (PSO), a widely used metaheuristic inspired by swarm intelligence, has shown considerable promise in feature selection tasks. However, conventional PSO often suffers from premature convergence and limited exploration capabilities, particularly in high-dimensional spaces. To overcome these limitations, this study proposes an enhanced PSO framework incorporating Orthogonal… More >