Guangyu Xu^1,2, Siyuan Xu³, Siyu Lu^4,*, Yuxin Liu¹, Bo Yang¹, Junmin Lyu⁵, Wenfeng Zheng^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4037-4053, 2025, DOI:10.32604/cmes.2025.074901 - 23 December 2025

Abstract Robust stereo disparity estimation plays a critical role in minimally invasive surgery, where dynamic soft tissues, specular reflections, and data scarcity pose major challenges to traditional end-to-end deep learning and deformable model-based methods. In this paper, we propose a novel disparity estimation framework that leverages a pretrained StyleGAN generator to represent the disparity manifold of Minimally Invasive Surgery (MIS) scenes and reformulates the stereo matching task as a latent-space optimization problem. Specifically, given a stereo pair, we search for the optimal latent vector in the intermediate latent space of StyleGAN, such that the photometric reconstruction… More >

Chen Pan, ChangGyoon Lim^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3747-3781, 2025, DOI:10.32604/cmes.2025.073389 - 23 December 2025

Abstract Solar forecasting using ground-based sky image offers a promising approach to reduce uncertainty in photovoltaic (PV) power generation. However, existing methods often rely on deterministic predictions that lack diversity, making it difficult to capture the inherently stochastic nature of cloud movement. To address this limitation, we propose a new two-stage probabilistic forecasting framework. In the first stage, we introduce I-GPT, a multiscale physics-constrained generative model for stochastic sky image prediction. Given a sequence of past sky images, I-GPT uses a Transformer-based VQ-VAE. It also incorporates multi-scale physics-informed recurrent units (Multi-scale PhyCell) and dynamically weighted fuses… More >

Ravi Nahta¹, Nagaraj Naik^2,*, Srivinay³, Swetha Parvatha Reddy Chandrasekhara⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 461-487, 2025, DOI:10.32604/cmes.2025.063973 - 31 July 2025

Abstract The exponential growth of over-the-top (OTT) entertainment has fueled a surge in content consumption across diverse formats, especially in regional Indian languages. With the Indian film industry producing over 1500 films annually in more than 20 languages, personalized recommendations are essential to highlight relevant content. To overcome the limitations of traditional recommender systems—such as static latent vectors, poor handling of cold-start scenarios, and the absence of uncertainty modeling—we propose a deep Collaborative Neural Generative Embedding (C-NGE) model. C-NGE dynamically learns user and item representations by integrating rating information and metadata features in a unified neural More >

Pratik Jadhav¹, Vuppala Adithya Sairam¹, Niranjan Bhojane¹, Abhyuday Singh¹, Shilpa Gite^1,2, Biswajeet Pradhan^3,*, Mrinal Bachute¹, Abdullah Alamri⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3493-3517, 2025, DOI:10.32604/cmc.2025.060764 - 16 April 2025

Abstract Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time real-time. Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems. The low-cost thermal imaging software produces low-resolution thermal images in grayscale format, hence necessitating methods for improving the resolution and colorizing the images. The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images, followed by a sparse autoencoder for colorization of thermal images and a multimodal convolutional neural network for… More >

Shuqiao Liu¹, Zhao Zhang^2,*, Hongyan Zhou¹, Xuebo Chen¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 929-948, 2025, DOI:10.32604/cmc.2024.057319 - 03 January 2025

Abstract This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems. Combining Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis data with Variation Autoencoder (VAE) and Generative Adversarial Network (GAN) the network framework model (SAE-GAN), is proposed for environmental data reconstruction. The model combines two popular generative models, GAN and VAE, to generate features conditional on categorical data embedding after SWOT Analysis. The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data. Reconstructed data is… More >

Wu-Jiu Sun¹, Xiao Fan Liu^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4551-4573, 2024, DOI:10.32604/cmc.2024.057714 - 19 December 2024

Abstract Microblogging platforms like X (formerly Twitter) and Sina Weibo have become key channels for spreading information online. Accurately predicting information spread, such as users’ reposting activities, is essential for applications including content recommendation and analyzing public sentiment. Current advanced models rely on deep representation learning to extract features from various inputs, such as users’ social connections and repost history, to forecast reposting behavior. Nonetheless, these models frequently ignore intrinsic confounding factors, which may cause the models to capture spurious relationships, ultimately impacting prediction performance. To address this limitation, we propose a novel Debiased Reposting Prediction… More >

Aswathy Ravikumar, Harini Sriraman^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 563-578, 2023, DOI:10.32604/csse.2023.034710 - 20 January 2023

Abstract Deep neural networks are gaining importance and popularity in applications and services. Due to the enormous number of learnable parameters and datasets, the training of neural networks is computationally costly. Parallel and distributed computation-based strategies are used to accelerate this training process. Generative Adversarial Networks (GAN) are a recent technological achievement in deep learning. These generative models are computationally expensive because a GAN consists of two neural networks and trains on enormous datasets. Typically, a GAN is trained on a single server. Conventional deep learning accelerator designs are challenged by the unique properties of GAN,… More >

Yiming Huang^{1, *}, Hang Lei¹, Xiaoyu Li^{1, *}, Qingsheng Zhu², Wanghao Ren³, Xusheng Liu^{2, 4}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 445-458, 2020, DOI:10.32604/cmc.2020.010390 - 23 July 2020

Abstract In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm. The hybrid quantum-classical framework, which is constructed by a variational quantum circuit (VQC) and an optimizer, plays a key role in the latest quantum machine learning studies. Nevertheless, in these hybridframework-based quantum machine learning models, the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems. There are also few studies focused on comparing the performance of quantum generative models with… More >

Syed Falahuddin Quadri¹, Xiaoyu Li^1,*, Desheng Zheng², Muhammad Umar Aftab¹, Yiming Huang³

Journal on Big Data, Vol.1, No.1, pp. 1-7, 2019, DOI:10.32604/jbd.2019.05899

Abstract Given the glut of information on the web, it is crucially important to have a system, which will parse the information appropriately and recommend users with relevant information, this class of systems is known as Recommendation Systems (RS)-it is one of the most extensively used systems on the web today. Recently, Deep Learning (DL) models are being used to generate recommendations, as it has shown state-of-the-art (SoTA) results in the field of Speech Recognition and Computer Vision in the last decade. However, the RS is a much harder problem, as the central variable in the… More >

Xintao Duan^1,*, Haoxian Song¹, Chuan Qin², Muhammad Khurram Khan³

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 483-493, 2018, DOI:10.3970/cmc.2018.01798

Abstract In this paper, we propose a novel coverless image steganographic scheme based on a generative model. In our scheme, the secret image is first fed to the generative model database, to generate a meaning-normal and independent image different from the secret image. The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image. Thus, we only need to transmit the meaning-normal image which is not related to the secret image, and we can achieve the same effect as the More >