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

“…The final aesthetic of installed solid wood floors largely depends on the uniformity of color and texture. However, most current research does not address the simultaneous classification of both texture and color in solid wood floors, and there is a notable gap in studies focusing on the classification of color-difference wood floors and white-edge wood floors. …”
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Fault Diagnosis Based On Improved Information Entropy And 1dcnn For Marine Turbocharger Rotor With Variable Speed by Hu Lei, Hu Haoran, Hu Nao, Liu Luyuan, Dong Fei, Yang Jianguo, Zhong Jiahong

“…The sensitivity of the improved and traditional information entropy is evaluated using a variance analysis across the measurement points, and the most effective vibration measurement points are selected based on the improved information entropy. …”
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Multi-scale extreme climate disaster prediction model integrated with ConvLSTM: taking rainstorm and flood disaster as an example by Lei He, Yunhao He, Yongqiang Xia, Yuxia Li, Bin Liu, Siqi Zhang, Cunjie Zhang

“…As one of the most influential and harmful disasters in extreme climate events, rainstorm and flood threaten people’s lives, property and social stability seriously. …”
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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

“…Eliminating the continuum from a spectrum being analyzed and correctly identifying its unique absorption characteristics are crucial for material identification, traditionally achieved by the apparent continuum removal methods like using an Upper Convex Hull (UCH). Nevertheless, most of these methods struggle when baseline curvature exceeds certain limits, often combining distinct absorptions. …”
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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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Quantum Classical Algorithm for the Study of Phase Transitions in the Hubbard Model via Dynamical Mean-Field Theory by Anshumitra Baul, Herbert Fotso, Hanna Terletska, Ka-Ming Tam, Juana Moreno

“…Modeling many-body quantum systems is widely regarded as one of the most promising applications for near-term noisy quantum computers. …”
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On the Hybrid Algorithm for Retrieving Day and Night Cloud Base Height from Geostationary Satellite Observations by Tingting Ye, Zhonghui Tan, Weihua Ai, Shuo Ma, Xianbin Zhao, Shensen Hu, Chao Liu, Jianping Guo

“…Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. …”
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Data-Driven Proactive Early Warning of Grid Congestion Probability Based on Multiple Time Scales by Haobo Fu, Ruizhuo Wang, Bingxu Zhai, Yuanzhuo Li, Pengyuan Li, Rui Zhang, Haoyuan He, Siyang Liao

“…First, a multi-stage joint optimization feature selection model is constructed to capture the 12 feature sets that are most conducive to grid congestion warning from the massive grid history data containing 622 features. …”
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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

“…Core journals like Scientific Reports and Frontiers in Aging Neuroscience published the most articles in this field. Highly cited publications and burst references highlight important milestones in the development history. …”
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Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations by Deepanshu Lakra, Deepanshu Lakra, Shobhit Pipil, Prashant K. Srivastava, Suraj Kumar Singh, Manika Gupta, Rajendra Prasad

“…The performance analysis of RMSE, R-squared, and correlation coefficients revealed that the Random Forest (RF) and Convolutional Neural Network (CNN) models demonstrated superior performance for SM estimation over the wheat field. …”
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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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Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward by Sudeep Tanwar, Qasim Bhatia, Pruthvi Patel, Aparna Kumari, Pradeep Kumar Singh, Wei-Chiang Hong

“…In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. …”
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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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Tree semantic segmentation from aerial image time series by Venkatesh Ramesh, Arthur Ouaknine, David Rolnick

“…We also introduce a simple convolutional block for extracting spatio-temporal features from image time series, enabling the use of popular pretrained backbones and methods. …”
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Learning Spectral&#x2013;Spatial-Former Deep Prior for Hyperspectral Image Superresolution by Zeinab Dehghan, Jingxiang Yang, Abdolraheem Khader, Jian Fang, Liang Xiao

“…To overcome this, we integrated our model into a deep convolutional neural network enhanced by a Transformer module for regularization. …”
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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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A Novel Federated Learning Framework for Sustainable and Efficient Breast Cancer Classification System (FL-L₂CNN-BCDet) by Nesma Abd El-Mawla, Mohamed A. Berbar, Nawal A. El-Fishawy, Mohamed A. El-Rashidy

“…AI is now being used in a variety of applications to diagnose Breast Cancer (BC). However, most of the recent research has focused on centralized learning, which can pose privacy risks. …”
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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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