Nizar Zaghden¹, Emad Ibrahim², Mukaram Safaldin^2,*, Mahmoud Mejdoub³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1117-1147, 2025, DOI:10.32604/cmc.2025.061948 - 26 March 2025

Abstract The increasing elderly population has heightened the need for accurate and reliable fall detection systems, as falls can lead to severe health complications. Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques. This study introduces an advanced fall detection model integrating YOLOv8, Faster R-CNN, and Generative Adversarial Networks (GANs) to enhance accuracy and robustness. A modified YOLOv8 architecture serves as the core, utilizing spatial attention mechanisms to improve critical image regions’ detection. Faster R-CNN is employed for fine-grained human posture analysis, while GANs… More >

Wenchang Yu¹, Xiaoqin Ma^1,2, Zheqing Zhang¹, Qinli Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1195-1218, 2025, DOI:10.32604/cmc.2025.060833 - 26 March 2025

Abstract Feature selection methods rooted in rough sets confront two notable limitations: their high computational complexity and sensitivity to noise, rendering them impractical for managing large-scale and noisy datasets. The primary issue stems from these methods’ undue reliance on all samples. To overcome these challenges, we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection algorithm. Firstly, we construct a robust fuzzy relation by introducing a truncation parameter. Then, based on this fuzzy relation, we propose the concept of cross-similarity, which emphasizes the sample-to-sample similarity relations… More >

RAHUL KUMAR SINGH^1,#, SUROJIT MANDAL^1,#, ADRIJA MOHANTA¹, RITU YADAV¹, RAJIV RANJAN KUMAR¹, RINKU KHATKAR¹, VIVEK UTTAM¹, UTTAM SHARMA¹, MANJIT KAUR RANA², MANJU JAIN³, HARDEEP SINGH TULI⁴, AKLANK JAIN^1,*

Oncology Research, Vol.33, No.3, pp. 591-604, 2025, DOI:10.32604/or.2024.050548 - 28 February 2025

Abstract Objectives: Recently, we and others have demonstrated the involvement of Zinc Finger Antisense 1 (ZFAS1) in cancer development. However, the intricate interplay of ZFAS1 with miRNAs and mRNAs remains to be fully understood. Materials and methods: We followed PRISMA guidelines to retrieve and assess the available literature on the topic “ZFAS1/miRNA/mRNA axis” and “Cancer” from databases such as PubMed, Google Scholar, and ScienceDirect. We also used bioinformatic webtools for analyzing the potential miRNA targets of ZFAS1 and its role in survival of cancer patients along with their role in various biological functions and pathways. Results: Our literature search… More >

Honglin Wang¹, Yangyang Zhang^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3399-3417, 2025, DOI:10.32604/cmc.2024.058932 - 17 February 2025

Abstract Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction… More >

Haider H. Al-Mahmood^1,*, Saad N. Alsaad²

Intelligent Automation & Soft Computing, Vol.40, pp. 53-74, 2025, DOI:10.32604/iasc.2024.059691 - 10 January 2025

Abstract In secure communications, lightweight encryption has become crucial, particularly for resource-constrained applications such as embedded devices, wireless sensor networks, and the Internet of Things (IoT). As these systems proliferate, cryptographic approaches that provide robust security while minimizing computing overhead, energy consumption, and memory usage are becoming increasingly essential. This study examines lightweight encryption techniques utilizing chaotic maps to ensure secure data transmission. Two algorithms are proposed, both employing the Logistic map; the first approach utilizes two logistic chaotic maps, while the second algorithm employs a single logistic chaotic map. Algorithm 1, including a two-stage mechanism… More >

Xunguang Yan^1,2, Wenrui Wang¹, Fanglin Lu¹, Hongyong Fan³, Bo Wu¹, Jianfeng Yu^1,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1439-1458, 2025, DOI:10.32604/cmc.2024.057797 - 03 January 2025

Abstract To maintain the reliability of power systems, routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues. The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods, especially in identifying small objects in high-resolution images. This study presents an enhanced object detection algorithm based on the Faster Region-based Convolutional Neural Network (Faster R-CNN) framework, specifically tailored for detecting small-scale electrical components like insulators, shock hammers, and screws in transmission line. The algorithm features an improved backbone network for Faster R-CNN, which significantly boosts the More >

S. A. Kamil^a,b,*, G. Jose^c

Chalcogenide Letters, Vol.21, No.1, pp. 11-20, 2024, DOI:10.15251/CL.2024.211.11

Abstract Er³⁺-ions doped SiO₂-ZnO-Na₂O thin films were fabricated using ultrafast laser plasma doping (ULPD) techniques under different ambient atmospheres; vacuum, nitrogen, oxygen and argon gas. The thickness of the layer produced depends on the ambient atmosphere during fabrication. The layer fabricated under a vacuum is the thinnest among all of the samples. In addition, the surface layer for the sample fabricated under a vacuum environment seems to be relatively smoother compared with those of the others. XRD patterns show that all samples are in a mixed amorphous-crystalline phase. All the Raman spectra exhibited a similar pattern, except More >

Yuzhe Ma, Jiye Zhang^*, Jiawei Shi

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.12, pp. 2843-2865, 2024, DOI:10.32604/fdmp.2024.055205 - 23 December 2024

Abstract The large number and dense layout of rail fastening can significantly affect the aerodynamic performance of trains. Utilizing the Improved Delayed Detached Eddy Simulation (IDDES) approach based on the SST (Shear Stress Transport) k-ω turbulent model, this study evaluates the effects of the rail fastening system on the aerodynamic force, slipstream and train wake under crosswind conditions. The results indicate that in such conditions, compared to the model without rails, the rail and the fastening system reduce the drag force coefficient of the train by 1.69%, while the lateral force coefficients increase by 1.16% and… More >

Kristine Meile^1,*, Martins Romanovskis^1,2, Thomas Nicol³, Neil Hindle³, Aivars Zhurinsh¹

Journal of Renewable Materials, Vol.12, No.12, pp. 2135-2152, 2024, DOI:10.32604/jrm.2024.056775 - 20 December 2024

Abstract Fast pyrolysis of pre-treated birch wood in a super-heated steam environment produces a condensate rich in anhydrosugars. With the objective to obtain several product streams from this condensate, the possibility of extracting additional chemical species is explored, thus promoting the development of a pyrolysis-based biorefinery. In this work, the extraction and recovery of pyrolytic phenols from birch wood pyrolysis condensate was studied using ion exchange resins. With an aim to achieve effective phenol recovery, while obtaining high purity levoglucosan, basic ion exchange resins, both in OH⁻ and Cl⁻ form, as well as polystyrene-divinyl resins without functional… More > Graphic Abstract

Yasunori Okano^1,*, Tsuyoshi Miyamoto¹, Sadik Dost²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011685

Abstract We have developed a machine learning model, called Hybrid-PINNs (Physics Informed Neural Networks), and applied for fast predictions of transport structures (flow and thermal fields) in the silicon (Si) melt during the Czochralski (Cz) bulk single crystal growth. Si bulk single crystals are mostly grown by the Cz method. For the growth of high-quality Si crystals with this method, it is essential to understand and control these transport structures in the melt. Since the direct observation of such transport fields in the melt during growth is usually impossible, numerical simulations provide a powerful tool for… More >