An Algorithm for the Shape-Based Distance of Microseismic Time Series Waveforms and Its Application in Clustering Mining Events by Hao Luo, Ziyu Liu, Song Ge, Linlin Ding, Li Zhang

“…MDCAE extracts low-dimensional features from waveform signals through multi-scale fusion and dilated convolutions while introducing the concept of waveform volatility (Vol) to capture variations in microseismic waveforms. An improved Shape-Based Distance (SBD) algorithm is then employed to measure the similarity of these features. …”
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Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction by Mehak Gurnani, Konstantinos Patlatzoglou, Joseph Barker, Derek Bivona, Libor Pastika, Ewa Sieliwonczyk, Boroumand Zeidaabadi, Paolo Inglese, Lara Curran, Ahran D. Arnold, Declan O'Regan, Zachary Whinnett, Kenneth C. Bilchick, Nicholas S. Peters, Daniel B. Kramer, Jonathan W. Waks, Arunashis Sau, Fu Siong Ng

“…First, we trained a variational autoencoder on 1.1 million ECGs and discovered 51 latent features that showed high disentanglement and ECG reconstruction accuracy. …”
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Clinical information prompt-driven retinal fundus image for brain health evaluation by Nuo Tong, Ying Hui, Shui-Ping Gou, Ling-Xi Chen, Xiang-Hong Wang, Shuo-Hua Chen, Jing Li, Xiao-Shuai Li, Yun-Tao Wu, Shou-Ling Wu, Zhen-Chang Wang, Jing Sun, Han Lv

“…Results The proposed framework yielded average RMSE, PSNR, and SSIM values of 98.23, 35.78 dB, and 0.64, respectively, which significantly outperformed 5 other methods: multi-channel Variational Autoencoder (mcVAE), Pixel-to-Pixel (Pixel2pixel), transformer-based U-Net (TransUNet), multi-scale transformer network (MT-Net), and residual vision transformer (ResViT). …”
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Protocol for predicting single- and multiple-dose-dependent gene expression using deep generative learning by Derek E. Bowman, Vishal Panda, Daniel Marri, Omar Kana, Sudin Bhattacharya

“…Summary: Variational autoencoders (VAEs) can be used to model the gene expression space of single-cell RNA sequencing (scRNA-seq) data. …”
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Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study by Samer El Kababji, Nicholas Mitsakakis, Elizabeth Jonker, Ana-Alicia Beltran-Bless, Gregory Pond, Lisa Vandermeer, Dhenuka Radhakrishnan, Lucy Mosquera, Alexander Paterson, Lois Shepherd, Bingshu Chen, William Barlow, Julie Gralow, Marie-France Savard, Christian Fesl, Dominik Hlauschek, Marija Balic, Gabriel Rinnerthaler, Richard Greil, Michael Gnant, Mark Clemons, Khaled El Emam

“…Four different generative models were evaluated: sequential synthesis with decision trees, Bayesian network, generative adversarial network, and a variational autoencoder. These generative models were compared to sampling with replacement (ie, bootstrap) as a simple alternative. …”
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Optimal Res-UNET architecture with deep supervision for tumor segmentation by Rahman Maqsood, Fazeel Abid, Jawad Rasheed, Jawad Rasheed, Jawad Rasheed, Onur Osman, Shtwai Alsubai

“…However, optimizing U-Net variants to enhance performance and computational efficiency remains challenging.ObjectiveTo develop an optimized Residual U-Net (Res-UNET) architecture enhanced by deep supervision techniques to improve segmentation accuracy of brain tumors on MRI datasets, specifically addressing challenges of conventional segmentation methods.MethodsThe study implemented a detailed evaluation of multiple U-Net variations, including basic U-Net, Res-UNet with Autoencoder regularization, and attention-enhanced U-Net architectures. …”
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Machine learning-based coalbed methane well production prediction and fracturing parameter optimization by HU Qiujia, LIU Chunchun, ZHANG Jianguo, CUI Xinrui, WANG Qian, WANG Qi, LI Jun, HE Shan

“…Furthermore, the absence of tailored fracturing designs has caused substantial variations in post-fracturing production performance among adjacent wells. …”
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Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems by Mohamed Medani, Ghada Moh. Samir Elhessewi, Mohammed Alqahtani, Somia A. Asklany, Sulaiman Alamro, Da’ad Albalawneh, Menwa Alshammeri, Mohammed Assiri

“…There is no treatment for dementia yet; therefore, the early detection and identification of persons at greater risk of emerging dementia becomes crucial, as this might deliver an opportunity to adopt lifestyle variations to decrease the risk of dementia. Many dementia risk prediction techniques to recognize individuals at high risk have progressed in the past few years. …”
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A secure IoT-edge architecture with data-driven AI techniques for early detection of cyber threats in healthcare by Mamta Kumari, Mahendra Gaikwad, Salim A. Chavan

“…VAE Model Performance: Variational Autoencoders achieved top accuracy (91.61%) in detecting IoMT cyberattacks. …”
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Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach by Xia Sheng, Yike Gui, Jie Yu, Yitian Wang, Zhenghao Li, Xiaoya Zhang, Yuxin Xing, Yuqing Wang, Zhaojun Li, Mingyue Zheng, Liquan Yang, Xutong Li

“…Our approach utilizes a variational autoencoder (VAE) generative model integrated with reinforcement learning for multi-objective optimization. …”
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Preliminary Study of Airfoil Design Synthesis Using a Conditional Diffusion Model and Smoothing Method by Kazuo Yonekura, Yuta Oshima, Masaatsu Aichi

“…Generative models such as generative adversarial networks and variational autoencoders are widely used for design synthesis. …”
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Text to Image Generation: A Literature Review Focus on the Diffusion Model by Zhou Jingxi

“…The main approaches in this area are Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DM). …”
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An Innovative Stepwise C‐Means Clustering Approach for Classification of Adolescent Idiopathic Scoliosis by Jiale Gong, Zifang Zhang, Yunzhang Cheng, Liang Cheng, Yating Dong, Lin Sha, Qin Fan, Jian Chen, Chaomeng Wu, Wenyuan Sui, Yaqing Zhang, Fuyun Liu, Weiming Hu, Wenqing Wei, Junlin Yang

“…Compared to direct clustering, the iterative method not only improves geometric interpretability but also enhances classification accuracy by better identifying subtle variations in spinal curvature. It further improves specificity, particularly in distinguishing sagittal and axial plane deformities, which are often overlooked in 2D systems. …”
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Using a Solution Construction Algorithm for Cyclic Shift Network Coding under Multicast Network to the Transformation of Musical Performance Styles by Xiuqin Wang, Jun Geng, Zhiyuan Li

“…A two-way recurrent neural network based on the gated recurrent unit is used to extract a sequence of note feature vectors of different styles, and a one-dimensional convolutional neural network is used to predict the intensity of the extracted note feature vector sequence for a specific style, which better learns the intensity variation of different styles of MIDI music.…”
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Music Generation Using Deep Learning and Generative AI: A Systematic Review by Rohan Mitra, Imran Zualkernan

“…The study examines common data representations in music generation, including raw waveforms, spectrograms, and MIDI, alongside the most prominent deep learning architectures like Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Transformer-based models. …”
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Metasurface-Based Solar Absorption Prediction System Using Artificial Intelligence by Md. Mottahir Alam, Ahteshamul Haque, Asif Irshad Khan, Samir Kasim, Amjad Ali Pasha, Aasim Zafar, Kashif Irshad, Anis Ahmad Chaudhary, Md. Samsuzzaman, Rezaul Azim

“…Moreover, Golden Eagle Optimization (GE)-based deep AlexNet algorithm is proposed for predicting the parameter variation and their effect on absorbance. The optimization technique is used to increase the effectiveness of the solar absorber by optimizing the design parameters. …”
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Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model by Leyi Wang, Yiming Wang, Xiaoyu Hu, Hui Wang, Ruilin Zhou

“…The performance of DIFF-MP is compared with that of generative adversarial networks and variational autoencoders. The results demonstrate that DIFF-MP consistently outperforms these models in terms of prediction error, coverage ratio, and spread–skill correlation. …”
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Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing by Suchuan Xing, Yihan Wang, Wenhe Liu

“…This paper proposes a dual-channel deep learning framework that integrates Temporal Convolutional Networks with Variational Autoencoders to address these challenges. …”
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Generative artificial intelligence in diabetes healthcare by Josep Vehi, Omer Mujahid, Aleix Beneyto, Ivan Contreras

“…This article explores key deep generative models, including variational autoencoders, generative adversarial networks, transformers, and diffusion models applied to tabular, time series, image, and text data. …”
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Transformer-Driven Inverse Learning for AI-Powered Ceramic Material Innovation With Advanced Data Preprocessing by Murad Ali Khan, Syed Shehryar Ali Naqvi, Muhammad Faseeh, Do-Hyeun Kim

“…K-Nearest Neighbors (KNN) imputation was first applied, improving data accuracy and completeness to address data gaps. Subsequently, Variational Autoencoders (VAE) were used for data augmentation, enriching the dataset’s diversity. …”
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