Research progress and prospect of intelligent prediction and disaster risk assessment of open-pit mining surface deformation by LI Hui, ZHU Wancheng, XU Xiaodong, SONG Qingwei, HAN Xiaofei, GENG Huikai

“…Specifically, by reviewing the intelligent monitoring technologies of mine surface deformation, this study indicates that the choice of intelligent monitoring methods should factor in data accuracy, installation cost and post-processing speed, reviews the intelligent modeling methods of surface deformation prediction regarding the methodological combination of traditional deformation prediction and intelligent optimization, machine learning and deep learning, and summarizes the mechanism behind the typical risk assessment method of mine deformation hazards. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…The development of automated models requires extensive labeled and incredibly abnormal data to accurately identify and analyze abnormalities, which are difficult to obtain in sufficient quantities. Although the deep learning-based architecture has yielded state-of-the-art performance in medical image anomaly detection, it cannot be generalized well because of the lack of anomalous datasets. …”
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Predictive Maintenance and Fault Detection for Motor Drive Control Systems in Industrial Robots Using CNN-RNN-Based Observers by Chanthol Eang, Seungjae Lee

“…We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. …”
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Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review by Kaelan Lockhart, Juan Sandino, Narmilan Amarasingam, Richard Hann, Barbara Bollard, Felipe Gonzalez

“…Despite the potential of established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, and Support Vector Machine, and gradient boosting in the semantic segmentation of UAV-captured images, there is a notable scarcity of research employing Deep Learning (DL) models in these extreme environments. …”
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Deep Convolutional Neural Network Model for the Differential Diagnosis of Schizophrenia Using EEG Signals by Filiz Demirdöğen, Çağla Danacı, Seda Arslan Tuncer, Mustafa Akkuş, Sevler Yıldız

“…In the classification phase, ResNet18, ResNet50 and EfficientNet models, which are FastAI, and Convolutional Neural Network (CNN) based deep learning models, were used. Results: Despite the complexity of electroencephalography data, CNN-based models in the study were successful in capturing different aspects of neurophysiological activity. …”
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Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning by Brandon K. Phan, Kuan-Hsuan Shen, Rishi Gurnani, Huan Tran, Ryan Lively, Rampi Ramprasad

“…By amalgamating high throughput generated simulation data with available experimental data for gas permeability, diffusivity, and solubility for various gases, we construct multi-task deep learning models. These models can simultaneously predict all three properties for all gases under consideration, with markedly enhanced predictive accuracy, particularly compared to traditional models reliant solely on experimental data for a singular property. …”
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Optimisation of sparse deep autoencoders for dynamic network embedding by Huimei Tang, Yutao Zhang, Lijia Ma, Qiuzhen Lin, Liping Huang, Jianqiang Li, Maoguo Gong

“…However, the existing deep learning‐based NE methods are time‐consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters. …”
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Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN by Ali Erbey, Necaattin Barışçı

“…With the growing success of deep learning architectures, research on lip reading has gained momentum. …”
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Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages by Muhammad Kashif Nazir, Cm Nadeem Faisal, Muhammad Asif Habib, Haseeb Ahmad

“…Additionally, the proposed model outperformed other transformer-based models, as well as deep learning and machine learning algorithms in sentiment extraction from code-mixed data. …”
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Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients. by Mikkel Bonde, Alexander Bonde, Haytham Kaafarani, Andreas Millarch, Martin Sillesen

“…We hypothesize that novel deep learning network approaches through transfer learning may be superior to legacy approaches for PoC risk prediction in the PDAC surgical setting.…”
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An online intelligent electronic medical record system via speech recognition by Xin Xia, Yunlong Ma, Ye Luo, Jianwei Lu

“…The rapid development of deep learning–based speech recognition technology promises to improve this situation. …”
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Gesture Recognition System Based on Time-Frequency Point Density of sEMG by Qiang Wang, Yao Chen, Chunhua Sheng, Shuaidi Song

“…It is usually realized by extracting the characteristics of different finger movements and then using machine learning or deep learning algorithms to classify and recognize them. …”
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Force Measurement Technology of Vision‐Based Tactile Sensor by Bin Fang, Jie Zhao, Nailong Liu, Yuhao Sun, Shixin Zhang, Fuchun Sun, Jianhua Shan, Yiyong Yang

“…According to the learning approach, force measurement methods are classified into physical and deep learning models. Further, branches of each method are analyzed in terms of input types. …”
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Web-based platform to collect, share and manage technical data of historical systemic architectures: the Telegraphic Towers along the Madrid-Valencia path by Margherita Lasorella, Pasquale de-Dato, Elena Cantatore

“…The platform takes advantage of digital models, machine and deep learning procedures and relational databases, in a GIS-based environment, for the recognition and categorisation of prevalent physical and qualitative features of systemic architectures, the recognition and qualification of dominant and recurrent decays and the management of recovery activities in a semi-automatic way. …”
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EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism by Jayoti Bansal, Gaurav Gangwar, Mohammad Aljaidi, Ali Alkoradees, Gagandeep Singh

“…With the use of innovative deep learning techniques, this research seeks to improve the diagnosis of ADHD using EEG data. …”
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A Novel Semi-Supervised Learning Method Based on Fast Search and Density Peaks by Fei Gao, Teng Huang, Jinping Sun, Amir Hussain, Erfu Yang, Huiyu Zhou

“…In addition, our algorithm is compared against several semi-supervised deep learning methods with similar structures. Experimental results demonstrate that the proposed algorithm has better stability than these methods.…”
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Design of an integrated model with temporal graph attention and transformer-augmented RNNs for enhanced anomaly detection by Sai Babu Veesam, Aravapalli Rama Satish, Sreenivasulu Tupakula, Yuvaraju Chinnam, Krishna Prakash, Shonak Bansal, Mohammad Rashed Iqbal Faruque

“…We introduce a new framework to address such challenges by incorporating state-of-the-art deep learning models that improve temporal and spatial context modeling. …”
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Fast Dynamic Time Warping and Hierarchical Clustering with Multispectral and Synthetic Aperture Radar Temporal Analysis for Unsupervised Winter Food Crop Mapping by Hsuan-Yi Li, James A. Lawarence, Philippa J. Mason, Richard C. Ghail

“…Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. …”
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Abstractive Summarization of Historical Documents: A New Dataset and Novel Method Using a Domain-Specific Pretrained Model by Keerthana Murugaraj, Salima Lamsiyah, Christoph Schommer

“…Experimental results on our constructed dataset demonstrate that our HistBERTSum-Abs method outperforms recent state-of-the-art deep learning-based methods and achieves results comparable to state-of-the-art LLMs in zero-shot settings in terms of ROUGE-1, ROUGE-2, and ROUGE-L F1 scores. …”
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Multi-source meteorological data assessment on daily runoff simulation in the upper reaches of the Hei River, Northwest China by Huazhu Xue, Yaheng Wang, Guotao Dong, Chenchen Zhang, Yaokang Lian, Hui Wu

“…Study region: The upper reaches of the Hei River Basin, northwest China Study focus: To improve the accuracy and physical consistency of runoff simulations, as well as to compare the applicability of meteorological data obtained from multiple sources, this study integrates physical mechanisms with deep learning methods to construct a coupled model, HIMS-LSTM. …”
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