Cloud-edge collaborative data anomaly detection in industrial sensor networks. by Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

“…However, existing research on sensor data anomaly detection for industrial sensor networks still has several inherent limitations. First, most detection models usually consider centralized detection. …”
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Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data by Byeongcheol Kang, Kyungbook Lee

“…The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). After simulated production data are used to train the classification model, the most possible TI can be selected when the observed production responses are put into the trained model. …”
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Rep-MobileViT: Texture and Color Classification of Solid Wood Floors Based on a Re-Parameterized CNN-Transformer Hybrid Model by Anning Duanmu, Sheng Xue, Zhenye Li, Yajun Zhang, Chao Ni

“…Specifically, the RepAIRB module is introduced, incorporating an asymmetric convolutional block (ACB) and a re-parameterized structure within the inverted residual block (IRB) module to enhance the network’s receptive field without increasing computational costs. …”
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Segmented Curve-Fitting Method for Continuum Removal in CRISM MTRDR data by P. Kumari, S. Soor, A. Shetty, S. G. Koolagudi

“…The identification score is improved by around 8% for the similarity matching method Weighted Sum of Spectrum Correlation and by around 1.5% for a Convolutional Neural Network. Furthermore, an SCF-based mineral identification framework demonstrates its effectiveness in identifying the dominant minerals on CRISM MTRDR hyperspectral data collected from different locations on the Martian surface.…”
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UNestFormer: Enhancing Decoders and Skip Connections With Nested Transformers for Medical Image Segmentation by Adnan Md Tayeb, Tae-Hyong Kim

“…Precise identification of organs and lesions in medical images is essential for accurate disease diagnosis and analysis of organ structures. Deep convolutional neural network (CNN)-based U-shaped networks are among the most popular and promising approaches for this task. …”
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A review of deep learning models to detect malware in Android applications by Elliot Mbunge, Benhildah Muchemwa, John Batani, Nobuhle Mbuyisa

“…The study revealed that convolutional neural networks, gated recurrent neural networks, deep neural networks, bidirectional long short-term memory, long short-term memory (LSTM) and cubic-LSTM are the most prominent deep learning-based malicious software detection models in Android applications. …”
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Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000 by Yabin Zhang, Lei Yu, Yuting Lv, Tiantian Yang, Qi Guo

“…High-frequency keywords include “alzheimer’s disease,” “parkinson’s disease,” “magnetic resonance imaging,” “convolutional neural network,” “biomarkers,” “dementia,” “classification,” “mild cognitive impairment,” “neuroimaging,” and “feature extraction.” …”
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Leveraging deep learning technology for enhancing printing press quality by Omotunde Alabi Muyiwa, B.H. Adejumo

“…<p>Machine learning technique usage for printing quality control is yet to be adopted in most printing press in Nigeria. However, deep learning technology can be used to improve printing quality. …”
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Lesion classification and diabetic retinopathy grading by integrating softmax and pooling operators into vision transformer by Chong Liu, Weiguang Wang, Jian Lian, Wanzhen Jiao

“…Therefore, plenty of automated screening technique have been developed to address this task.MethodsAmong these techniques, the deep learning models have demonstrated promising outcomes in various types of machine vision tasks. However, most of the medical image analysis-oriented deep learning approaches are built upon the convolutional operations, which might neglect the global dependencies between long-range pixels in the medical images. …”
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Advances in weed identification using hyperspectral imaging: A comprehensive review of platform sensors and deep learning techniques by Bright Mensah, Nitin Rai, Kelvin Betitame, Xin Sun

“…Techniques like image calibration, standard normal variate, multiplicative scatter correction, Savitsky-Golay smoothing, derivatives, and features selection are among the most used techniques, (d) traditional machine learning models namely support vector machines (SVM), partial least square discriminant analysis (PLS-DA), maximum likelihood classifiers (MLC), and random forest (RF) are the widely employed classifiers for weed identification, (e) the application of deep learning technique, namely convolutional neural networks (CNNs) are limited, but its application demonstrated superior performance accuracies compared to traditional machine learning models. …”
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Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports by Ying Wang, Farah Magrabi

“…The default parameters of BERT were found to be the most optimal configuration. For incident type, fine-tuned BERT achieved high F-scores above 89% across all test datasets (CNNs=81%). …”
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BiLSTM-Based Parallel CNN Models With Attention and Ensemble Mechanism for Twitter Sentiment Analysis by Anas W. Abulfaraj

“…When used together, models like the Convolutional Neural Networks (CNN) and LSTM networks have significant high-performance results for text feature extraction and semantic relationship of the word. …”
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On the effectiveness of neural operators at zero-shot weather downscaling by Saumya Sinha, Brandon Benton, Patrick Emami

“…We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. …”
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PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs) by Wafa Alanazi, Di Meng, Gianluca Pollastri

“…Today, deep learning is arguably the most powerful method for predicting RSA and other structural features of proteins. …”
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YOLO-SegNet: A Method for Individual Street Tree Segmentation Based on the Improved YOLOv8 and the SegFormer Network by Tingting Yang, Suyin Zhou, Aijun Xu, Junhua Ye, Jianxin Yin

“…In urban forest management, individual street tree segmentation is a fundamental method to obtain tree phenotypes, which is especially critical. Most existing tree image segmentation models have been evaluated on smaller datasets and lack experimental verification on larger, publicly available datasets. …”
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Reliable Event Detection via Multiple Edge Computing on Streaming Traffic Social Data by Yipeng Ji, Jingyi Wang, Yan Niu, Hongyuan Ma

“…The results indicate that our model can better implement streaming social traffic event detection, and is superior to most text classification methods.…”
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TCBGY net for enhanced wear particle detection in ferrography using self attention and multi scale fusion by Lei He, Haijun Wei, Cunxun Sun

“…Secondly, we introduce the convolutional block attention module (CBAM) into the neck network to enhance salience for detecting wear particles while suppressing irrelevant information interference. …”
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Federated and ensemble learning framework with optimized feature selection for heart disease detection by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

“…The ensemble-based approaches proved the most predictive after testing several different machine learning (ML) models, including random forests, the light gradient boosting machine, support vector machines, k-nearest neighbors, convolutional neural networks, and long short-term memory. …”
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Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…It has been learned that image-processing techniques overwhelm the existing research and have the potential to integrate meteorological data. The most widely used algorithms incorporate Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and MobileNet with accuracy rates between 64.3 and 100%. …”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

“…Recently, the feasibility of learning feature detection tasks using supervised learning with convolutional neural networks (CNNs) has been demonstrated. …”
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