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 >

Qi Wang¹, Chenxin Li^1,*, Chichen Lin², Weijian Fan³, Shuang Feng¹, Yuanzhong Wang⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3351-3369, 2024, DOI:10.32604/cmc.2024.057191 - 18 November 2024

Abstract News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem. Most previous works only extract features and evaluate media from one dimension independently, ignoring the interconnections between different aspects. This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features. This framework models the relationship and interaction between media bias and factuality, utilizing this relationship to assist in the prediction of profiling results. Our approach extracts features independently while aligning and fusing them through recursive convolution and More >

Sameema Tariq¹, Ata-Ur- Rehman^2,3, Maria Abubakar², Waseem Iqbal⁴, Hatoon S. Alsagri⁵, Yousef A. Alduraywish⁵, Haya Abdullah A. Alhakbani^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2493-2515, 2024, DOI:10.32604/cmc.2024.055787 - 18 November 2024

Abstract In video surveillance, anomaly detection requires training machine learning models on spatio-temporal video sequences. However, sometimes the video-only data is not sufficient to accurately detect all the abnormal activities. Therefore, we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video data. This paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual data. The proposed model is trained to produce low reconstruction error… More >

Priyanka¹, Damodarakurup Sajeev², Shaik Ahmed³, Shankar Pidishety³, Ram Soorat^3,*

Journal of Quantum Computing, Vol.6, pp. 53-66, 2024, DOI:10.32604/jqc.2024.054000 - 14 November 2024

Abstract The present study delves into the application of investigating quantum state behaviour, particularly focusing on coherent and superposition states. These states, characterized by their remarkable stability and precision, have found extensive utility in various domains of quantum mechanics and quantum information processing. Coherent states are valuable for manipulating quantum systems with accuracy. Superposition states allow quantum systems to exist in numerous configurations at the same time, which paves the way for quantum computing’s capacity for parallel processing. The research accentuates the crucial role of quantum error correction (QEC) in ensuring the stability and reliability of… More >

Yihao Chen¹, Yongxing Shen^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011251

Abstract The phase field method for fracture has become mainstream for fracture simulation. It transforms the crack nucleation problem into a minimization problem of the sum of the elastic potential energy and the crack surface energy. Because of the biconvexity of its energy functional, there is an energy barrier between local minima with and without a crack, resulting it difficult for standard methods, such as the Newton method, to converge to a cracked solution when starting from a solid without crack, especially when the material and the geometry are uniform, even if current cracked solution with… More >

Huilin Ren^1,2, David W. Rosen², Yi Xiong^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.010920

Abstract The advancement of continuous fiber-reinforced polymer additive manufacturing (CFRP-AM) enables the fabrication of structures with complex geometries and superior properties. However, current design methodologies consider toolpath design and structure optimization as separate stages, with toolpath design typically serving as a post-processing step after structure optimization. This sequential methodology limits the full exploitation of fiber reinforced polymer composites (FRPC) capabilities, particularly in achieving optimal structural integrity and manufacturability. In this paper, a manufacturing-oriented method is proposed for designing continuous FRPC structures, in which the structural layout and continuous fiber toolpaths are simultaneously optimized. The integrated design… More >

Muhammad Akbar^1,2, Huali Pan^1,*, 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 >

Jing Geng^1,*, Huali Yang², Shengze Hu³

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 985-986, 2024, DOI:10.32604/iasc.2024.059591 - 31 October 2024

Abstract This article has no abstract. More >

Mumtaz Khan^1,*, Muhammad Shoaib Anwar², Mudassar Imran³, Amer Rasheed⁴

Frontiers in Heat and Mass Transfer, Vol.22, No.5, pp. 1399-1419, 2024, DOI:10.32604/fhmt.2024.056597 - 30 October 2024

Abstract This study employs the Buongiorno model to explore nanoparticle migration in a mixed convection second-grade fluid over a slendering (variable thickness) stretching sheet. The convective boundary conditions are applied to the surface. In addition, the analysis has been carried out in the presence of Joule heating, slips effects, thermal radiation, heat generation and magnetohydrodynamic. This study aimed to understand the complex dynamics of these nanofluids under various external influences. The governing model has been developed using the flow assumptions such as boundary layer approximations in terms of partial differential equations. Governing partial differential equations are… More >