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 >

Tao Yan¹, Javed Rashid^2,3, Muhammad Shoaib Saleem^3,4, Sajjad Ahmad⁴, Muhammad Faheem^5,*

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 >

Jian Su¹, Haijian Shao^1,2,*, Xing Deng¹, Yingtao Jiang²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2219-2242, 2024, DOI:10.32604/cmc.2024.057604 - 18 November 2024

Abstract The rapidly advancing Convolutional Neural Networks (CNNs) have brought about a paradigm shift in various computer vision tasks, while also garnering increasing interest and application in sensor-based Human Activity Recognition (HAR) efforts. However, the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems. This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain, which reduces the model’s depth and accelerates activity inference. Unlike traditional pruning methods that focus on the spatial domain and the importance of filters, this… More >

Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

Hina Bhanbhro^1,*, Yew Kwang Hooi¹, Mohammad Nordin Bin Zakaria¹, Worapan Kusakunniran², Zaira Hassan Amur¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2243-2259, 2024, DOI:10.32604/cmc.2024.052138 - 18 November 2024

Abstract Object detection has made a significant leap forward in recent years. However, the detection of small objects continues to be a great difficulty for various reasons, such as they have a very small size and they are susceptible to missed detection due to background noise. Additionally, small object information is affected due to the downsampling operations. Deep learning-based detection methods have been utilized to address the challenge posed by small objects. In this work, we propose a novel method, the Multi-Convolutional Block Attention Network (MCBAN), to increase the detection accuracy of minute objects aiming to… More >

Yifei Li¹, Ruisen Yang¹, Dan Xie^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.013359

Abstract Cavity closed with a thin plate is a common structure in aircrafts, such as landing gear compartments and skin skeletons. The plate undergoing aerodynamic pressure on top is generally vibrating in the amplitude of thickness, which will induce an acoustic pressure in the cavity underneath and it will further affect the panel response. Considering both aerodynamic and acoustic pressure on the panel, there will be an interest to investigate the aero-acoustic-structure coupling mechanism and the aeroacoustoelastic response of the plate. Von Karman plate theory, piston theory and two-dimensional partial differential acoustic equation are employed for… More >

Bo Liu^1,2,*, Jia Xie², Gonghua Chen², Yugang Gong², Hongliang Yao¹, Tiegang Li¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012904

Abstract Biodegradable metal implants that meet clinical applications require good mechanical properties and an appropriate biodegradation rate. Additively manufactured (AM) biodegradable zinc (Zn) alloys constitute an essential branch of orthopedic implants because of their moderate degradation and bone-mimicking mechanical properties. This paper investigated the microstructural evolution and corrosion mechanisms of zinc-copper (Zn-Cu) alloys prepared by the laser-powder-bed-fusion (L-PBF) additive manufacturing method. Alloying with Cu significantly increases the ultimate tensile strength (UTS) of unalloyed Zn, but the UTS and ductility of unalloyed Zn and Zn-2Cu decrease with increasing laser energy density. Unalloyed Zn has a dendritic microstructure,… More >

Erdong Shen¹, Ting Pan¹, Pan Guo¹, Ke Chen¹, Rui Xu¹, Mei Yang¹, Dahe Zhan¹, Fang Fang¹, Qinghui Wu^1,*, Jianbing Hu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-3, 2024, DOI:10.32604/icces.2024.012772

Abstract Objective
This study investigates the synergistic effects of a novel multifunctional nanoparticle on gastric cancer treatment through photothermal therapy, chemotherapy, and immunotherapy.

Method
Synthesize hollow mesoporous Prussian blue nanoparticles and load them with luteolin. Use exosomes to encapsulate the nanoparticles and modify the surface of the targeted peptide GX1. Detect the morphology of nanoparticles using a nanoparticle size analyzer and transmission electron microscopy. Use Coomassie Brilliant Blue to detect the effect of extracellular vesicle encapsulation. Detect the thermal conversion efficiency of nanoparticles under specific laser irradiation through infrared and ultraviolet spectroscopy, as well as the release rate… More >

HAO WU^#, NA HUO^#, SITUO WANG, ZIWEI LIU, YI JIANG^*, QUAN SHI^*

BIOCELL, Vol.48, No.11, pp. 1531-1542, 2024, DOI:10.32604/biocell.2024.054278 - 07 November 2024

Abstract Stem cells have shown great application potential in wound repair, tissue regeneration, and disease treatment. Therefore, a full understanding of stem cells and their related regulatory mechanisms in disease treatment is conducive to improving the therapeutic effect of stem cells. However, thus far, there are still many unsolved mysteries in the field of stem cells due to technical limitations, which hinder the in-depth exploration of stem cells and their wide clinical application. Single-cell sequencing (SCS) has provided very powerful and unbiased insights into cell gene expression profiles at the single-cell level, bringing exciting results to More >

Donghua Dai^1,2,*, Yanze Li^1,2, Dongdong Gu^1,2,*, Wentai Zhao^1,2, Yuhang Long^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012500

Abstract Laser Directed Energy Deposition (LDED) technology was employed to fabricate internal structures within the hollow interiors of rotating parts, such as tubes and cylinders. A three-dimensional transient multiphysics model for C276 material was developed, which anticipated the impact of angular velocity from tube rotation on various aspects. This model, validated by experiments, focused on the melt pool morphology, Marangoni convection, oriented crystal microevolution, and deposited material microhardness. It was found that at 150 ms deposition, the dimensions of the melt pool stabilized. With an increase in the Peclet number, heat transfer within the melt pool… More >