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  • 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

    Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT

    Arar Al Tawil1,*, Laiali Almazaydeh2, Doaa Qawasmeh3, Baraah Qawasmeh4, Mohammad Alshinwan1,5, Khaled Elleithy6

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3395-3412, 2024, DOI:10.32604/cmc.2024.057279 - 18 November 2024

    Abstract Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information, a practice known as phishing. This study utilizes three distinct methodologies, Term Frequency-Inverse Document Frequency, Word2Vec, and Bidirectional Encoder Representations from Transformers, to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks. The study uses feature extraction methods to assess the performance of Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron algorithms. The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). Word2Vec’s 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

    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

    REVIEW

    Impact of nanoparticles on immune cells and their potential applications in cancer immunotherapy

    JYOTHI B. NAIR1,2, ANU MARY JOSEPH3, SANOOP P.4, MANU M. JOSEPH5,*

    BIOCELL, Vol.48, No.11, pp. 1579-1602, 2024, DOI:10.32604/biocell.2024.054879 - 07 November 2024

    Abstract Nanoparticles represent a heterogeneous collection of materials, whether natural or synthetic, with dimensions aligning in the nanoscale. Because of their intense manifestation with the immune system, they can be harvested for numerous bio-medical and biotechnological advancements mainly in cancer treatment. This review article aims to scrutinize various types of nanoparticles that interact differently with immune cells like macrophages, dendritic cells, T lymphocytes, and natural killer (NK) cells. It also underscores the importance of knowing how nanoparticles influence immune cell functions, such as the production of cytokines and the presentation of antigens which are crucial for… More >

  • Open Access

    PROCEEDINGS

    Finite Element Modelling of Composite Armor Against 7.62 mm Projectile Impact

    Lei Peng1,*, Jin Zhou2, Xianfeng Zhang3, Zhongwei Guan4,5

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011196

    Abstract This paper presents the numerical modelling of the ballistic response of hybrid composite structures subjected to 7.62 mm projectile impact. This study focuses on the modelling of composites made of various materials, including ceramics, Ultra-High-Molecular-Weight Polyethylene (UHMWPE), Kevlar, and compressed wood, with fabrication of hybrid laminated structures that offer promising ballistic resistance capabilities. By employing a range of constitutive models and failure criteria, the finite element model simulates the ballistic behaviors of the constituent materials, facilitating a comprehensive understanding of their performance under high-velocity impacts. The core of the study lies in the comparison between… More >

  • Open Access

    PROCEEDINGS

    Collision-Induced Adhesion Behavior and Mechanism for Metal Particle and Graphene

    Haitao Hei1, Jian Wang1, Yonggang Zheng1, Hongfei Ye1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011298

    Abstract Micro- and nano-scale collisions are widely involved in molecular movement, drug delivery, the actuation of micro-nano devices, etc. They often exhibit extraordinary behaviour relative to the common macroscopic collisions. A deep understanding on the scale reduction-induced novel collision phenomenon and the related mechanism is rather crucial. In this work, the comprehensive impact behaviour of metal projectiles on graphene is investigated on the basis of molecular dynamics simulations. It is found that besides the common penetration and rebound behaviours, the impacting metal projectile can also be captured by the ultrasoft two-dimensional materials, i.e., the adhesion behaviour.… More >

  • Open Access

    PROCEEDINGS

    Mesoscopic Modelling and Optimization of Additive-Manufactured Microlattice Materials for Energy Absorption

    Lijun Xiao1,*, Weidong Song1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.010981

    Abstract Additively-manufactured microlattice materials have attracted much attention due to their outstanding mechanical properties and energy absorption capacity. Considering the high cost of 3D printing, numerical simulation has become the most common approach for predicting and optimizing the mechanical performance of micro-lattice materials. The current study provides an efficient method to incorporate the printing process induced geometric defects in the lattice models. Numerical simulations are performed to precisely predict the mechanical response of the printed microlattice materials under quasi-static and dynamic loadings. Furthermore, the microlattice structures are graphically represented based on their mesoscopic structural characteristics. Accordingly, More >

  • Open Access

    CORRECTION

    Correction: Influence of Various Earth-Retaining Walls on the Dynamic Response Comparison Based on 3D Modeling

    Muhammad Akbar1,2, Huali Pan1,*, Jiangcheng Huang3, Bilal Ahmed4, Guoqiang Ou1

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2625-2625, 2024, DOI:10.32604/cmes.2024.059706 - 31 October 2024

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Segmentation of Head and Neck Tumors Using Dual PET/CT Imaging: Comparative Analysis of 2D, 2.5D, and 3D Approaches Using UNet Transformer

    Mohammed A. Mahdi1, Shahanawaj Ahamad2, Sawsan A. Saad3, Alaa Dafhalla3, Alawi Alqushaibi4, Rizwan Qureshi5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2351-2373, 2024, DOI:10.32604/cmes.2024.055723 - 31 October 2024

    Abstract The segmentation of head and neck (H&N) tumors in dual Positron Emission Tomography/Computed Tomography (PET/CT) imaging is a critical task in medical imaging, providing essential information for diagnosis, treatment planning, and outcome prediction. Motivated by the need for more accurate and robust segmentation methods, this study addresses key research gaps in the application of deep learning techniques to multimodal medical images. Specifically, it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution. The primary research questions guiding this study… More >

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