A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features by Xing‐bo Cai, Ze‐hui Lu, Zhi Peng, Yong‐qing Xu, Jun‐shen Huang, Hao‐tian Luo, Yu Zhao, Zhong‐qi Lou, Zi‐qi Shen, Zhang‐cong Chen, Xiong‐gang Yang, Ying Wu, Sheng Lu

“…To develop and validate an intelligent classifier for distal radius fractures by combining a statistical shape model (SSM) with a neural network (NN) based on CT imaging data. Methods From August 2022 to May 2023, a total of 80 CT scans were collected, including 43 normal radial bones and 37 distal radius fractures (17 Colles', 12 Barton's, and 8 Smith's fractures). …”
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Estimation of Rice Leaf Nitrogen Content Using UAV-Based Spectral–Texture Fusion Indices (STFIs) and Two-Stage Feature Selection by Xiaopeng Zhang, Yating Hu, Xiaofeng Li, Ping Wang, Sike Guo, Lu Wang, Cuiyu Zhang, Xue Ge

“…A total of 18 vegetation indices (VIs), 40 texture features (TFs), and 27 STFIs were derived from UAV images. To optimize the feature set, a two-stage feature selection strategy was employed, combining Pearson correlation analysis with model-specific embedded selection methods: Recursive Feature Elimination with Cross-Validation (RFECV) for Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and Sequential Forward Selection (SFS) for Support Vector Regression (SVR) and Deep Neural Networks (DNNs). …”
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KediNet: A Hybrid Deep Learning Architecture for Thai Dessert Recognition by Nawanol Theera-Ampornpunt, Panisa Treepong, Naphadon Phokabut, Sarawut Phlaichana

“…Our main contributions include: 1) the proposal of KediNet, which outperforms nine state-of-the-art deep learning models; 2) a comprehensive comparison of classification performance across different model architectures, image resolutions, and data augmentation techniques; and 3) the creation of THDESS-20, a new publicly available dataset containing 5,242 images across 20 classes of Thai desserts. …”
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Evaluation of spatial visual perception of streets based on deep learning and spatial syntax by Mingyang Yu, Xin Chen, Xiangyu Zheng, Weikang Cui, Qingrui Ji, Huaqiao Xing

“…Through spatial visualization of street quality, overlay analysis with network accessibility, and multiple linear regression, it examines the correlations between street space quality and its constituent elements. …”
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Predicting Student Dropout Through Text and Media Content Analysis of VKontakte Profiles by Sergei S. Gorshkov, Dmitry I. Ignatov, Anastasia Yu. Chernysheva

“…Image and video content were analyzed using scene recognition models (Places365) and contextual alignment models (CLIP), while text data were processed using the BERTopic model for topic modeling and a pre-trained model for emotion analysis. …”
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Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging. by Lena Lim, Andre Marquand, Ana A Cubillo, Anna B Smith, Kaylita Chantiluke, Andrew Simmons, Mitul Mehta, Katya Rubia

“…The discriminating GM patterns showed higher classification weights for ADHD in earlier developing ventrolateral/premotor fronto-temporo-limbic and stronger classification weights for healthy controls in later developing dorsolateral fronto-striato-parieto-cerebellar networks. Several regions were also decreased in GM in ADHD relative to healthy controls in the univariate VBM analysis, suggesting they are GM deficit areas.…”
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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data by Mussadiq Abdul Rahim, Sultan Daud Khan, Salabat Khan, Muhammad Rashid, Rafi Ullah, Hanan Tariq, Stanislaw Czapp

“…In this research we propose a GPS-only data trajectory analysis and a novel scheme to convert GPS trajectory data to image-based data to train a custom Convolutional Neural Network (CNN) model. …”
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Small-Sample Authenticity Identification and Variety Classification of <i>Anoectochilus roxburghii</i> (Wall.) Lindl. Using Hyperspectral Imaging and Machine Learning by Yiqing Xu, Haoyuan Ding, Tingsong Zhang, Zhangting Wang, Hongzhen Wang, Lu Zhou, Yujia Dai, Ziyuan Liu

“…Hyperspectral data were collected from the front and back leaves of nine species of Goldthread and two counterfeit species (Bloodleaf and Spotted-leaf), followed by classification using a variety of machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN). …”
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The Fusion of Focused Spectral and Image Texture Features: A New Exploration of the Nondestructive Detection of Degeneration Degree in <i>Pleurotus geesteranus</i> by Yifan Jiang, Jin Shang, Yueyue Cai, Shiyang Liu, Ziqin Liao, Jie Pang, Yong He, Xuan Wei

“…In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image data were acquired from <i>Pleurotus geesteranus</i> strains exhibiting varying degrees of degradation, followed by preprocessing using Savitzky–Golay smoothing (SG), multivariate scattering correction (MSC), and standard normal variate transformation (SNV). …”
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A real-time predicting online tool for detection of people’s emotions from Arabic tweets based on big data platforms by Naglaa Abdelhady, Ibrahim E. Elsemman, Taysir Hassan A. Soliman

“…Abstract Emotion prediction is a subset of sentiment analysis that aims to extract emotions from text, speech, or images. …”
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Distinct bone metabolic networks identified in Phospho1−/− mice vs. wild type mice using [18F]FDG total-body PET by Abigail F. Hellman, Paul S. Clegg, Colin Farquharson, José Luis Millán, Carlos J. Alcaide-Corral, Carlos J. Alcaide-Corral, Karla J. Suchacki, Karla J. Suchacki, Adriana A. S. Tavares, Adriana A. S. Tavares

“…IntroductionTotal-body PET is a recent development in clinical imaging that produces large datasets involving multiple tissues, enabling the use of new analytical methods for multi-organ assessments, such as network analysis—a well-developed method in neuroimaging. …”
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Root segmentation of horticultural plants in X-Ray CT images by integrating 2D instance segmentation with 3D point cloud clustering by Mary E. Cassity, Paul C. Bartley, Yin Bao

“…Accurate root segmentation from CT images is integral to studying RSA. Research studies on segmenting roots from CT images have been mainly limited to image processing-based approaches which may require parameter tuning and often lack common segmentation metrics, e.g., Dice and IoU. …”
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MHAGuideNet: a 3D pre-trained guidance model for Alzheimer’s Disease diagnosis using 2D multi-planar sMRI images by Yuanbi Nie, Qiushi Cui, Wenyuan Li, Yang Lü, Tianqing Deng

“…Traditional computer-aided diagnostic methods using structural MRI data often focus on capturing such features but face challenges, like overfitting with 3D image analysis and insufficient feature capture with 2D slices, potentially missing multi-planar information, and the complementary nature of features across different orientations. …”
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A hybrid steganography framework using DCT and GAN for secure data communication in the big data era by Kaleem Razzaq Malik, Muhammad Sajid, Ahmad Almogren, Tauqeer Safdar Malik, Ali Haider Khan, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen

“…This study introduces a novel and comprehensive steganography framework using the discrete cosine transform (DCT) and the deep learning algorithm, generative adversarial network. By leveraging deep learning techniques in both spatial and frequency domains, the proposed hybrid architecture offers a robust solution for applications requiring high levels of data integrity and security. …”
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A Synergy Between Machine Learning and Formal Concept Analysis for Crowd Detection by Anas M. Al-Oraiqat, Oleksandr Drieiev, Sattam Almatarneh, Mohammadnoor Injadat, Karim A. Al-Oraiqat, Hanna Drieieva, Yassin M. Y. Hasan

“…Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. …”
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Analyzing the Efficacy of Computer-Aided Detection in Cerebral Aneurysm Diagnosis Using MRI Modality: A Review by Keerthi A. S. Pillai, Preena K. P., Madhu S. Nair

“…CNNs have proven to be a crucial component in improving the accuracy and efficiency of aneurysm detection by automatically learning features from raw imaging data, bypassing the need for manual feature extraction. …”
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Swin Transformer With Late-Fusion Feature Aggregation for Multi-Modal Vehicle Reidentification by Reza Fuad Rachmadi, Supeno Mardi Susiki Nugroho, I. Ketut Eddy Purnama

“…Vehicle reidentification is a problem that coexisted with the advent of CCTV technology for road monitoring. Vehicle image data from low-light environments is very challenging for reidentification tasks, and multi-modal data (visible, near-infrared, and thermal) is often used to improve model performance. …”
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Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysisRes... by Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang, Xiaoer Wei, Shundong Hu, Yuwu Zhao, Li Cao, Jun Wang, Saman Nazarian, Ying Cao, Lan Zheng, Min Zhang, Jianliang Fu, Jingbo Li, Xiang Han, Dahong Qian, Dong Huang

“…We aim to create a novel end-to-end artificial intelligence (AI) model that uses MRI data to rapidly identify high AF risk in patients who suffer from an acute ischemic stroke. …”
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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenari... by Cecilia Parracciani, Daniela Gigante, Onisimo Mutanga, Stefania Bonafoni, Marco Vizzari

“…We integrated harmonic modeling, Gray-Level Co-occurrence Matrix (GLCM) textural analysis, statistical image and gradient analysis, and other spectral and Digital Terrain Model (DTM)-derived indices to enhance the classification capabilities. …”
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Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation by John Adebisi, Sesham Srinu, Varqa Mitonga

“…CNNs have shown excellent results in plant disease classification based on image data set. This study proposes the potential of aligning existing software-based Central Processing Units (CPUs) and Graphic Processing Units (GPUs) with FPGA-based potato disease classification using CNNs. …”
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