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  • Open Access

    ARTICLE

    Machine Learning Model for Wind Power Forecasting Using Enhanced Multilayer Perceptron

    Ahmed A. Ewees1,2,*, Mohammed A. A. Al-Qaness3, Ali Alshahrani1, Mohamed Abd Elaziz4

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2287-2303, 2025, DOI:10.32604/cmc.2025.061320 - 16 April 2025

    Abstract Wind power forecasting plays a crucial role in optimizing the integration of wind energy into the grid by predicting wind patterns and energy output. This enhances the efficiency and reliability of renewable energy systems. Forecasting approaches inform energy management strategies, reduce reliance on fossil fuels, and support the broader transition to sustainable energy solutions. The primary goal of this study is to introduce an effective methodology for estimating wind power through temporal data analysis. This research advances an optimized Multilayer Perceptron (MLP) model using recently proposed metaheuristic optimization algorithms, namely the Fire Hawk Optimizer (FHO)… More >

  • Open Access

    ARTICLE

    YOLO-SIFD: YOLO with Sliced Inference and Fractal Dimension Analysis for Improved Fire and Smoke Detection

    Mariam Ishtiaq1,2, Jong-Un Won1,2,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5343-5361, 2025, DOI:10.32604/cmc.2025.061466 - 06 March 2025

    Abstract Fire detection has held stringent importance in computer vision for over half a century. The development of early fire detection strategies is pivotal to the realization of safe and smart cities, inhabitable in the future. However, the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets, lack of diversity, and class imbalance. In this work, we explore the possible ways forward to overcome these challenges posed by available datasets. We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art (SOTA) vision-based models and proposeMore >

  • Open Access

    ARTICLE

    An Adaptive Firefly Algorithm for Dependent Task Scheduling in IoT-Fog Computing

    Adil Yousif*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2869-2892, 2025, DOI:10.32604/cmes.2025.059786 - 03 March 2025

    Abstract The Internet of Things (IoT) has emerged as an important future technology. IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data. In IoT-Fog computing, resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers. The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem. This study proposes an Adaptive Firefly Algorithm (AFA) for… More >

  • Open Access

    ARTICLE

    Forest Fire Severity Level Using dNBR Spectral Index

    Nur Nabihah Ghazali1, Noraain Mohamed Saraf1,*, Abdul Rauf Abdul Rasam1,*, Ainon Nisa Othman1, Siti Aekbal Salleh1, Nurhafiza Md Saad2

    Revue Internationale de Géomatique, Vol.34, pp. 89-101, 2025, DOI:10.32604/rig.2025.057562 - 24 February 2025

    Abstract Forest fires are contributing significantly to the acceleration of deforestation. Monitoring and mapping these fires are crucial, and remote sensing technology has proven effective for this purpose. This research employs remote sensing methods to evaluate the severity of a forest fire in Kampung Balai Besar, Dungun. The incident, covering a 23-hectare area, occurred on 15 June 2021. Initial data processing utilized Sentinel-2 satellite images from 14 June 2021 (pre-fire) and 19 June 2021 (post-fire). The extent and severity of the fire were assessed using the Normalized Burn Ratio (NBR) index derived from satellite images. Different… More >

  • Open Access

    ARTICLE

    YOLO-LFD: A Lightweight and Fast Model for Forest Fire Detection

    Honglin Wang1, Yangyang Zhang2,*, 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 >

  • Open Access

    ARTICLE

    Engine Misfire Fault Detection Based on the Channel Attention Convolutional Model

    Feifei Yu1, Yongxian Huang2,*, Guoyan Chen1, Xiaoqing Yang2, Canyi Du2,*, Yongkang Gong2

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 843-862, 2025, DOI:10.32604/cmc.2024.058051 - 03 January 2025

    Abstract To accurately diagnose misfire faults in automotive engines, we propose a Channel Attention Convolutional Model, specifically the Squeeze-and-Excitation Networks (SENET), for classifying engine vibration signals and precisely pinpointing misfire faults. In the experiment, we established a total of 11 distinct states, encompassing the engine’s normal state, single-cylinder misfire faults, and dual-cylinder misfire faults for different cylinders. Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840 Hz. The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and… More >

  • Open Access

    ARTICLE

    MARIE: One-Stage Object Detection Mechanism for Real-Time Identifying of Firearms

    Diana Abi-Nader1, Hassan Harb2, Ali Jaber1, Ali Mansour3, Christophe Osswald3, Nour Mostafa2,*, Chamseddine Zaki2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 279-298, 2025, DOI:10.32604/cmes.2024.056816 - 17 December 2024

    Abstract Security and safety remain paramount concerns for both governments and individuals worldwide. In today’s context, the frequency of crimes and terrorist attacks is alarmingly increasing, becoming increasingly intolerable to society. Consequently, there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces, thereby preventing potential attacks or violent incidents. Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection, particularly in identifying firearms. This paper introduces a novel automatic firearm detection surveillance system, utilizing a one-stage detection… More >

  • Open Access

    ARTICLE

    Hybrid Metaheuristic Lion and Firefly Optimization Algorithm with Chaotic Map for Substitution S-Box Design

    Arkan Kh Shakr Sabonchi*

    Journal of Information Hiding and Privacy Protection, Vol.6, pp. 21-45, 2024, DOI:10.32604/jihpp.2024.058954 - 31 December 2024

    Abstract Substitution boxes (S-boxes) are key components of symmetrical cryptosystems, acting as nonlinear substitution functions that hide the relationship between the encrypted text and input key. This confusion mechanism is vital for cryptographic security because it prevents attackers from intercepting the secret key by analyzing the encrypted text. Therefore, the S-box design is essential for the robustness of cryptographic systems, especially for the data encryption standard (DES) and advanced encryption standard (AES). This study focuses on the application of the firefly algorithm (FA) and metaheuristic lion optimization algorithm (LOA), thereby proposing a hybrid approach called the… More >

  • Open Access

    ARTICLE

    Greener, Safer Packaging: Carbon Nanotubes/Gelatin-Enhanced Recycled Paper for Fire Retardation with DFT Calculations

    Hebat-Allah S. Tohamy*

    Journal of Renewable Materials, Vol.12, No.12, pp. 1963-1983, 2024, DOI:10.32604/jrm.2024.054977 - 20 December 2024

    Abstract Fire retardant CNTs/WPP/Gel composite papers were fabricated by incorporating bio-based carbon nanotubes (CNTs) recycled from mature beech pinewood sawdust (MB) and cellulosic waste printed paper (WPP) into a gelatin solution (Gel) and allowing the mixture to dry at room temperature. The CNTs within the WPP matrix formed a network, enhancing the mechanical and thermal properties of the resulting CNTs paper sheet. In comparison to pure WPP/Gel, CNTs/WPP/Gel exhibited superior flexibility, mechanical toughness, and notable flame retardancy characteristics. This study provides a unique and practical method for producing flame-retardant CNTs/WPP/Gel sheets, suitable for diverse industrial applications,… More > Graphic Abstract

    Greener, Safer Packaging: Carbon Nanotubes/Gelatin-Enhanced Recycled Paper for Fire Retardation with DFT Calculations

  • Open Access

    ARTICLE

    Enhancing Fire Detection Performance Based on Fine-Tuned YOLOv10

    Trong Thua Huynh*, Hoang Thanh Nguyen, Du Thang Phu

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2281-2298, 2024, DOI:10.32604/cmc.2024.057954 - 18 November 2024

    Abstract In recent years, early detection and warning of fires have posed a significant challenge to environmental protection and human safety. Deep learning models such as Faster R-CNN (Faster Region based Convolutional Neural Network), YOLO (You Only Look Once), and their variants have demonstrated superiority in quickly detecting objects from images and videos, creating new opportunities to enhance automatic and efficient fire detection. The YOLO model, especially newer versions like YOLOv10, stands out for its fast processing capability, making it suitable for low-latency applications. However, when applied to real-world datasets, the accuracy of fire prediction is… More >

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