Formation permeability estimation using mud loss data by deep learning by Yaser Abdollahfard, Seyed Morteza Mirabbasi, Mohammad Ahmadi, Abdolhossein Hemmati-Sarapardeh, Sefatallah Ashoorian

“…The mud loss rate data were generated at different sets of reservoir and drilling data values using a reservoir simulator and then evaluated by calculating the correlation coefficients to ensure their validity and to check the fit under real conditions. …”
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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The local feature extraction module utilizes dilated convolutional neural networks(CNN) with various kernel sizes to extract short-term temporal patterns at different scales. …”
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A Hybrid Method Combining Variational Mode Decomposition and Deep Neural Networks for Predicting PM2.5 Concentration in China by Senlin Li, Bo Tang, Xiaowu Deng

“…The deep neural structures used include recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs). To demonstrate the effectiveness of VDPS, we conducted comparative evaluations of different models’ performance on many experimental datasets of PM2.5 concentrations in four cities: Beijing, Shanghai, Guangzhou, and Chengdu. …”
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Prediction of adverse drug reactions based on pharmacogenomics combination features: a preliminary study by Mingxiu He, Mingxiu He, Yiyang Shi, Fangfang Han, Fangfang Han, Fangfang Han, Yongming Cai, Yongming Cai, Yongming Cai

“…The algorithm uses Convolutional Neural Networks (CNN) and cross-features to learn the latent drug-gene-ADR associations for ADRs prediction.Results and DiscussionThe performance of DGANet was compared to three state-of-the-art algorithms with different genomic features. …”
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SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification by Chunyang Wang, Chao Zhan, Bibo Lu, Wei Yang, Yingjie Zhang, Gaige Wang, Zongze Zhao

“…After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. …”
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SPCB-Net: A Multi-Scale Skin Cancer Image Identification Network Using Self-Interactive Attention Pyramid and Cross-Layer Bilinear-Trilinear Pooling by Xin Qian, Tengfei Weng, Qi Han, Chen Wu, Hongxiang Xu, Mingyang Hou, Zicheng Qiu, Baoping Zhou, Xianqiang Gao

“…Deep convolutional neural networks have made some progress in skin lesion classification and cancer diagnosis, but there are still some problems to be solved, such as the challenge of small inter-class feature differences and large intra-class feature differences, which might limit the classification performance of the model as high-level and low-level features are not properly utilized. …”
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CNN-ELM-BASED DEEP LEARNING FRAMEWORK FOR KNEE OSTEOARTHRITIS CLASSIFICATION FROM RADIOGRAPHIC IMAGES by V Srividhya, P Jega Juliet, N Neelima, Senthil Kumar Seeni

“…The CNN-ELM model system integrates Contrast Limited Adaptive Histogram Equalisation (CLAHE) in the preprocessing stage to enhance image quality and highlight subtle structural differences associated with KOA. The custom CNN composed of three convolutional layers extracts deep spatial features from the enhanced X-ray images and these features are passed to the ELM classifier, which performs fast, non-iterative learning using pseudo-inverse computations. …”
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Reducing overfitting in vehicle recognition by decorrelated sparse representation regularisation by Wanyu Wei, Xinsha Fu, Siqi Ma, Yaqiao Zhu, Ning Lu

“…Through ablation analysis, we find that DSR can drive the model to focus on the essential differences among all kinds of vehicles.…”
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Human-Annotated Label Noise and Their Impact on ConvNets for Remote Sensing Image Scene Classification by Longkang Peng, Tao Wei, Xuehong Chen, Xiaobei Chen, Rui Sun, Luoma Wan, Jin Chen, Xiaolin Zhu

“…Human-labeled training datasets are essential for convolutional neural networks (ConvNets) in satellite image scene classification. …”
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Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles by Diana-Andreea Sterpu, Daniel Măriuța, Grigore Cican, Ciprian-Marius Larco, Lucian-Teodor Grigorie

“…In this study, a hybrid deep learning model is proposed, combining convolutional neural networks (CNNs) and operating directly on raw airfoil geometry, with parallel branches of fully connected deep neural networks (DNNs) that process operational parameters and engineered features. …”
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Landfill Site Suitability Assessment Based on GIS and Multicriteria Analysis: A Case Study of Kirkuk City by Hana Kareem Shekho, Muntadher Aidi Shareef

“…The Analytic Hierarchy Process (AHP) was utilized for multi-criteria decision analysis of possible landfill sites, linear regression was employed for population projection, and a Convolutional Neural Network (CNN) was utilized for Normalized Difference Vegetation Index (NDVI)/ Normalized Difference Built-up Index (NDBI) prediction. …”
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A deep learning-based multi-view approach to automatic 3D landmarking and deformity assessment of lower limb by Reyhaneh Rostamian, Masoud Shariat Panahi, Morad Karimpour, Hadi G. Kashani, Amirhossein Abi

“…The average coordinate error (difference between automatically and manually determined coordinates) of the landmarks was 2.05 ± 1.36 mm on test data, whereas the average angular error (difference between automatically and manually calculated angles in three and two dimensions) on the same dataset was 0.53 ± 0.66° and 0.74 ± 0.87°, respectively. …”
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Hyperspectral Signatures for Detecting the Concrete Hydration Process Using Neural Networks by Shiming Li, Alfred Strauss, Damjan Grba, Maximilian Granzner, Benjamin Täubling-Fruleux, Thomas Zimmermann

“…This means that inadequate curing conditions lead to a loss of concrete quality and negative consequences in structural engineering. In addition, different state-of-the-art (SOTA) curing surface treatments and hydration periods have a significant effect on durability. …”
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Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers by Bernardo Luis Tuleski, Cristina Keiko Yamaguchi, Stefano Frizzo Stefenon, Leandro dos Santos Coelho, Viviana Cocco Mariani

“…Developing appropriate fault-labeled datasets can be challenging due to nonlinearity variations and divergence in feature distribution among different engine kinds or operating scenarios. To solve this task, this study experimentally measures audio emission signals from compression ignition engines in different vehicles, simulating injector failures, intake hose failures, and absence of failures. …”
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Comparative Analysis of Hybrid Attention and Progressive Layering Through a Comprehensive Evaluation of ARU-Net and PLU-Net in Brain Tumour Segmentation by Noman Ahmed Siddiqui, Muhammad Tahir Qadri, Muhammad Ovais Akhter, Tahira Anwar, Fahad Farooq

“…The preprocessing steps are quite different as ARU-Net uses simplified Z-score normalisation and resizing, and PLU-Net involves a full pipeline, involving skull stripping, bias field correction, and two normalisations. …”
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Dual-Branch Deep Learning with Dynamic Stage Detection for CT Tube Life Prediction by Zhu Chen, Yuedan Liu, Zhibin Qin, Haojie Li, Siyuan Xie, Litian Fan, Qilin Liu, Jin Huang

“…In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. …”
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Unbalancing Datasets to Enhance CNN Models Learnability: A Class-Wise Metrics-Based Closed-Loop Strategy Proposal by Somayeh Shahrabadi, Victor Alves, Emanuel Peres, Raul Morais Dos Santos, Telmo Adao

“…Using these datasets, 72 models with varying configurations – including different convolutional neural network architectures, initial learning rates, and optimizers – were initially trained and then evaluated against imagery test sets. …”
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A Malware Detection Method Based on Genetic Algorithm Optimized CNN-SENet Network by Zheng Yang, Hua Zhu, Zhao Li, Gang Wang, Meng Su

“…To this end, this paper proposes a malware detection method based on genetic algorithm optimization of the CNN-SENet network, which firstly introduces the SENet attention mechanism into the convolutional neural network to enhance the spatial feature extraction capability of the model; then, the application programming interface (API) sequences corresponding to different software behaviors are processed by segmentation and de-duplication, which in turn leads to the sequence feature extraction through the CNN-SENet model; finally, genetic algorithm is used to optimize the hyperparameters of CNN-SENet network to reduce the computational overhead of CNN and to achieve the recognition and classification of different malware at the output layer. …”
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TraitBertGCN: Personality Trait Prediction Using BertGCN with Data Fusion Technique by Muhammad Waqas, Fengli Zhang, Asif Ali Laghari, Ahmad Almadhor, Filip Petrinec, Asif Iqbal, Mian Muhammad Yasir Khalil

“…Abstract Personality prediction via different techniques is an established and trending topic in psychology. …”
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Spatio-Temporal Prediction of Surface Remote Sensing Data in Equatorial Pacific Ocean Based on Multi-Element Fusion Network by Tianliang Xu, Zhiquan Zhou, Chenxu Wang, Yingchun Li, Tian Rong

“…In this paper, we propose a multi-element fusion network model based on convolutional long short-term memory (ConvLSTM) and an attention mechanism to predict the SST and analyze the effects of different elemental inputs on the model’s prediction performance using the prediction results. …”
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