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 >

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 >

Lixiang Wang¹, Longfei Wen^2,3, Rong Tian^2,3,*, Chun Feng^1,4,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.011137

Abstract The extended finite element method (XFEM) has been successful in crack analysis but faces challenges in modeling multiple cracks. One challenge is the linear dependence and ill-conditioning of the global stiffness matrix, while another is the geometric description for multiple cracks. To address the first challenge, the Improved XFEM (IXFEM) [1–9] is extended to handle multiple crack problems, effectively eliminating issues of linear dependence and ill-conditioning. Additionally, to overcome the second challenge, a novel level set templated cover cutting method (LSTCCM) [10] is proposed, which combines the advantages of the level set method and cover More >

Alaa U. Khawaja¹, Ahmad Shaf^2,*, Faisal Al Thobiani³, Tariq Ali⁴, Muhammad Irfan⁵, Aqib Rehman Pirzada², Unza Shahkeel²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2399-2420, 2024, DOI:10.32604/cmes.2024.054257 - 31 October 2024

Abstract Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This study proposes FTCNNLSTM (Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory), an algorithm combining Convolutional Neural Networks, Long Short-Term Memory Networks, and Attentive Interpretable Tabular Learning. The model preprocesses the CWRU (Case Western Reserve University) bearing dataset using segmentation, normalization, feature scaling, and label encoding. Its architecture comprises multiple 1D Convolutional layers, batch normalization, max-pooling, and LSTM blocks with dropout, followed by batch normalization, dense layers, and More >

Rasha Almarshdi^1,2,*, Etimad Fadel¹, Nahed Alowidi¹, Laila Nassef¹

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 949-983, 2024, DOI:10.32604/iasc.2024.056792 - 31 October 2024

Abstract The Internet of Medical Things (IoMT) is one of the critical emerging applications of the Internet of Things (IoT). The huge increases in data generation and transmission across distributed networks make security one of the most important challenges facing IoMT networks. Distributed Denial of Service (DDoS) attacks impact the availability of services of legitimate users. Intrusion Detection Systems (IDSs) that are based on Centralized Learning (CL) suffer from high training time and communication overhead. IDS that are based on distributed learning, such as Federated Learning (FL) or Split Learning (SL), are recently used for intrusion… More >

Jiachang Liu^1,*, Zhengwei Huang², Junfeng Xiang¹, Lu Liu¹, Manlin Hu¹

Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

Lechan Yang^1,*, Li Li², Shouming Ma³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1471-1489, 2024, DOI:10.32604/cmc.2024.056379 - 15 October 2024

Abstract Accurate geospatial data are essential for geographic information systems (GIS), environmental monitoring, and urban planning. The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data. In this paper, we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery. Existing geospatial data recovery methods require complete datasets for training, resulting in time-consuming data recovery and lack of generalization. To address these issues, we propose a GAIN-LSTM-based geospatial data recovery method (TGAIN), which consists of two main works:… More >

Umar Zaman¹, Junaid Khan², Eunkyu Lee^1,3, Sajjad Hussain⁴, Awatef Salim Balobaid⁵, Rua Yahya Aburasain⁵, Kyungsup Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1789-1808, 2024, DOI:10.32604/cmc.2024.056222 - 15 October 2024

Abstract Maritime transportation, a cornerstone of global trade, faces increasing safety challenges due to growing sea traffic volumes. This study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System (AIS) data and advanced deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (DBLSTM), Simple Recurrent Neural Network (SimpleRNN), and Kalman Filtering. The research implemented rigorous AIS data preprocessing, encompassing record deduplication, noise elimination, stationary simplification, and removal of insignificant trajectories. Models were trained using key navigational parameters: latitude, longitude, speed, and heading. Spatiotemporal aware processing through trajectory segmentation… More >

Futai Liang^1,2, Xin Chen^1,*, Song He¹, Zihao Song¹, Hao Lu³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1101-1121, 2024, DOI:10.32604/cmc.2024.055326 - 15 October 2024

Abstract In the application of aerial target recognition, on the one hand, the recognition error produced by the single measurement of the sensor is relatively large due to the impact of noise. On the other hand, it is difficult to apply machine learning methods to improve the intelligence and recognition effect due to few or no actual measurement samples. Aiming at these problems, an aerial target recognition algorithm based on self-attention and Long Short-Term Memory Network (LSTM) is proposed. LSTM can effectively extract temporal dependencies. The attention mechanism calculates the weight of each input element and… More >

Anshul Mahajan^*, Sunil K. Singla

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1623-1649, 2024, DOI:10.32604/cmes.2024.054468 - 27 September 2024

Abstract The identification of individuals through ear images is a prominent area of study in the biometric sector. Facial recognition systems have faced challenges during the COVID-19 pandemic due to mask-wearing, prompting the exploration of supplementary biometric measures such as ear biometrics. The research proposes a Deep Learning (DL) framework, termed DeepBio, using ear biometrics for human identification. It employs two DL models and five datasets, including IIT Delhi (IITD-I and IITD-II), annotated web images (AWI), mathematical analysis of images (AMI), and EARVN1. Data augmentation techniques such as flipping, translation, and Gaussian noise are applied to More >