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  • Open Access

    PROCEEDINGS

    Deep Learning Aided Optimization of 1D Phononic Crystals

    Shih-Chun Liao1, I-Ling Chang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012885

    Abstract In this work, a new deep learning (DL) approach for the bandgap optimization of 1-D phononic crystal will be reported. The unit cell of the phononic crystal is composed of 4 layers with 3 materials, i.e., concrete, soil and rubber. A deep learning model is trained to replace the computationally demanding traditional solvers for the bandgap calculation of 1-D phononic crystals. Four variables, including material properties and layer thicknesses, will be taken into account. The predicted bandgap by the trained model is compared with that calculated by transfer matrix in order to check the accuracy More >

  • Open Access

    PROCEEDINGS

    Deep Learning-Based Prediction of Material Elastic Constants and Residual Stresses of Orthotropic Materials from Moiré Interferometry

    Dong-Wook Lee1,*, Heungjo An2, Tae Yeon Kim3, Sungmun Lee4, Jide Oyebanji1, Prabakaran Balasubramanian1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011286

    Abstract This work analyzes the problems of material elastic constants identification and residual stresses determination in an orthotropic materials using hole drilling method. These problems are very important to understand mechanical performance of materials. A lot of optical method such as Moiré, laser speckle interferometry, digital image correlation or photoelasticity is developed to estimate displacement (or strain) fields or applied loads (or stresses) from images. These methods require a very complicated techniques, skill, and efforts to analysis images. But deep learning method based on a convolution neural network shows better performance in image analysis problems such… More >

  • Open Access

    REVIEW

    A Comprehensive Overview and Comparative Analysis on Deep Learning Models

    Farhad Mortezapour Shiri*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

    Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

    Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

  • Open Access

    ARTICLE

    SAR-LtYOLOv8: A Lightweight YOLOv8 Model for Small Object Detection in SAR Ship Images

    Conghao Niu1,*, Dezhi Han1, Bing Han2, Zhongdai Wu2

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1723-1748, 2024, DOI:10.32604/csse.2024.056736 - 22 November 2024

    Abstract The high coverage and all-weather capabilities of Synthetic Aperture Radar (SAR) image ship detection make it a widely accepted method for maritime ship positioning and identification. However, SAR ship detection faces challenges such as indistinct ship contours, low resolution, multi-scale features, noise, and complex background interference. This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images, incorporating key structures to enhance performance. The YOLOv8 backbone is replaced by the Slim Backbone (SB), and the Delete Medium-sized Detection Head (DMDH) structure is eliminated to concentrate on shallow features. Dynamically adjusting the… More >

  • Open Access

    REVIEW

    A Survey of Lung Nodules Detection and Classification from CT Scan Images

    Salman Ahmed1, Fazli Subhan2,3, Mazliham Mohd Su’ud3,*, Muhammad Mansoor Alam3,4, Adil Waheed5

    Computer Systems Science and Engineering, Vol.48, No.6, pp. 1483-1511, 2024, DOI:10.32604/csse.2024.053997 - 22 November 2024

    Abstract In the contemporary era, the death rate is increasing due to lung cancer. However, technology is continuously enhancing the quality of well-being. To improve the survival rate, radiologists rely on Computed Tomography (CT) scans for early detection and diagnosis of lung nodules. This paper presented a detailed, systematic review of several identification and categorization techniques for lung nodules. The analysis of the report explored the challenges, advancements, and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning (DL) algorithm. The findings also highlighted the usefulness of DL… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Approach for Green Energy Forecasting in Asian Countries

    Tao Yan1, Javed Rashid2,3, Muhammad Shoaib Saleem3,4, Sajjad Ahmad4, Muhammad Faheem5,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024

    Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

  • Open Access

    REVIEW

    AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

    Mohd Asif Hajam1, Tasleem Arif1, Akib Mohi Ud Din Khanday2, Mudasir Ahmad Wani3,*, Muhammad Asim3,*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2077-2131, 2024, DOI:10.32604/cmc.2024.057136 - 18 November 2024

    Abstract The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and cost-effectiveness compared to modern drugs. Throughout the extensive history of medicinal plant usage, various plant parts, including flowers, leaves, and roots, have been acknowledged for their healing properties and employed in plant identification. Leaf images, however, stand out as the preferred and easily accessible source of information. Manual plant identification by plant taxonomists is intricate, time-consuming, and prone to errors, relying heavily on human perception. Artificial intelligence (AI) techniques offer a solution by automating plant recognition processes. This study thoroughly examines cutting-edge… More >

  • Open Access

    REVIEW

    AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice

    Mitra Madanchian1,*, Hamed Taherdoost1,2,3,4

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2133-2159, 2024, DOI:10.32604/cmc.2024.057094 - 18 November 2024

    Abstract This comparative review explores the dynamic and evolving landscape of artificial intelligence (AI)-powered innovations within high-tech research and development (R&D). It delves into both theoretical models and practical applications across a broad range of industries, including biotechnology, automotive, aerospace, and telecommunications. By examining critical advancements in AI algorithms, machine learning, deep learning models, simulations, and predictive analytics, the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies. The review integrates both qualitative and quantitative data derived from academic studies, industry reports, and real-world case studies to showcase the… More >

  • Open Access

    ARTICLE

    Enhancing Solar Energy Production Forecasting Using Advanced Machine Learning and Deep Learning Techniques: A Comprehensive Study on the Impact of Meteorological Data

    Nataliya Shakhovska1,2,*, Mykola Medykovskyi1, Oleksandr Gurbych1,3, Mykhailo Mamchur1,3, Mykhailo Melnyk1

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3147-3163, 2024, DOI:10.32604/cmc.2024.056542 - 18 November 2024

    Abstract The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability, reliability, and economic benefits. This study explores advanced machine learning (ML) and deep learning (DL) techniques for predicting solar energy generation, emphasizing the significant impact of meteorological data. A comprehensive dataset, encompassing detailed weather conditions and solar energy metrics, was collected and preprocessed to improve model accuracy. Various models were developed and trained with different preprocessing stages. Finally, three datasets were prepared. A novel hour-based prediction wrapper was introduced, utilizing external sunrise and sunset data to restrict… More >

  • Open Access

    ARTICLE

    HGNN-ETC: Higher-Order Graph Neural Network Based on Chronological Relationships for Encrypted Traffic Classification

    Rongwei Yu, Xiya Guo*, Peihao Zhang, Kaijuan Zhang

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2643-2664, 2024, DOI:10.32604/cmc.2024.056165 - 18 November 2024

    Abstract Encrypted traffic plays a crucial role in safeguarding network security and user privacy. However, encrypting malicious traffic can lead to numerous security issues, making the effective classification of encrypted traffic essential. Existing methods for detecting encrypted traffic face two significant challenges. First, relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic. Second, machine learning and convolutional neural network methods lack sufficient network expression capabilities, hindering the full exploration of traffic’s potential characteristics. To address these limitations, this study introduces a traffic classification method that utilizes… More >

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