Instance Segmentation of Sugar Apple (<i>Annona squamosa</i>) in Natural Orchard Scenes Using an Improved YOLOv9-seg Model by Guanquan Zhu, Zihang Luo, Minyi Ye, Zewen Xie, Xiaolin Luo, Hanhong Hu, Yinglin Wang, Zhenyu Ke, Jiaguo Jiang, Wenlong Wang

“…An Efficient Multiscale Attention (EMA) mechanism was added to strengthen feature representation across scales, addressing sugar apple variability and maturity differences. Additionally, a Convolutional Block Attention Module (CBAM) refined the focus on key regions and deep semantic features. …”
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Hybrid CNN–LSTM Model With Soft Attention Mechanism for Short‐Term Load Forecasting in Smart Grid by Syed Muhammad Hasanat, Muhammad Haris, Kaleem Ullah, Syed Zarak Shah, Usama Abid, Zahid Ullah

“…These methods optimize smart grid performance under variable conditions by leveraging the synergistic integration of multiple architectures. …”
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Development of a river dissolved oxygen prediction model integrating spatial effects and multiple deep learning algorithm by Yubo Zhao, Mo Chen

“…In addition, wavelet transform is used to explore the temporal correlations between DO and meteorological and water quality variables, further enhancing the interpretability of the deep learning approach. …”
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Artificial Intelligence (AI) approach for the quantification of C-phycocyanin in Spirulina platensis: Hybrid stacking-ensemble model based on machine learning and deep learning by Jun Wei Roy Chong, Kuan Shiong Khoo, Huong-Yong Ting, Iwamoto Koji, Zengling Ma, Pau Loke Show

“…This study proposes a hybrid stacking-ensemble model integrating convolutional neural networks (CNN) for automated feature extraction with both Support Vector Machine (SVM) and eXtreme gradient boosting (XGBoost) as base models and multiple meta-regressor models. …”
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SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction by Yu Shi, ShanLin Niu, Lei Wang, Liang Ye, YaoZong Zhang, HanYu Hong

“…In practical application scenarios, the aero-optical thermal radiation patterns in degraded images are not fixed, and types of aero-optics thermal radiation are more variable and complex. In order to handle multiple types of aero-optics thermal radiation effects effectively and to combine the advantages of image prior constraints and deep learning networks, we propose a surface fitting constrained multidimensional hybrid attention aero-optics thermal radiation correction network (SFMHANet) in this article. …”
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Intelligent recognition method for personnel intrusion hazardous area in fully mechanized mining face by Qinghua MAO, Jiao ZHAI, Xin HU, Yinan SU, Xusheng XUE

“…To address the problems of low accuracy of video AI recognition of personnel intrusion hazardous areas in fully mechanized mining face caused by factors such as variable personnel scales, and dynamic changes of hazardous areas, an intelligent recognition method for personnel intrusion hazardous areas of fully mechanized mining face based on RSCA-YOLOv8s and automatic division of hazardous areas is proposed. …”
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Deep Learning and Methods Based on Large Language Models Applied to Stellar Light Curve Classification by Yu-Yang Li, Yu Bai, Cunshi Wang, Mengwei Qu, Ziteng Lu, Roberto Soria, Jifeng Liu

“…In this study, we present a comprehensive evaluation of models based on deep learning and large language models (LLMs) for the automatic classification of variable star light curves, using large datasets from the Kepler and K2 missions. …”
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Multimodal Deep Learning Model for Cylindrical Grasp Prediction Using Surface Electromyography and Contextual Data During Reaching by Raquel Lázaro, Margarita Vergara, Antonio Morales, Ramón A. Mollineda

“…The results show that context has great predictive power. Variables such as object size and weight (product-related) were found to have a greater impact on model performance than task height (task-related). …”
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Solar Wind Speed Prediction via Graph Attention Network by Yanru Sun, Zongxia Xie, Haocheng Wang, Xin Huang, Qinghua Hu

“…Through visualization, we find GTA excavates the relationships between multiply variables without domain prior knowledge, which may help us find other unknown associations in heliophysics data sets. …”
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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…Advanced machine-learning approaches are being used to understand student performance variables as educational data grows. A big dataset from several Chinese institutions and high schools is used to develop a credible student performance prediction technique. …”
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MATHEMATICAL MODELS OF CREATION OF A SUBSYSTEM OF ENSURING SAFETY OF INFORMATION IN THE DISTRIBUTED INFORMATION SYSTEMS by D. O. Esikov, R. N. Akinshin, P. I. Abramov, L. E. Loutina

“…This problem is reduced to the kind of problems of integer linear programming with Boolean variables, this fact allows to apply the existing methods for its solvation. …”
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Force output in giant-slalom skiing: A practical model of force application effectiveness. by Matt R Cross, Clément Delhaye, Jean-Benoit Morin, Maximilien Bowen, Nicolas Coulmy, Frédérique Hintzy, Pierre Samozino

“…Ski athletes (N = 15) were equipped with ski-mounted force plates and a global navigation satellite system to compute the following variables over 14 turns: path length (L), velocity normalized energy dissipation [Δemech/vin], radial force [Fr], total force (both limbs [Ftot], the outside limb, and the difference between limbs), and a ratio of force application (RF = Fr/Ftot). …”
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Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence Tomography Imaging: Integration Into Clinical Decision Support Sy... by Michela Sperti, Camilla Cardaci, Francesco Bruno, Syed Taimoor Hussain Shah, Konstantinos Panagiotopoulos, Karim Kassem, Giuseppe De Nisco, Umberto Morbiducci, Raffaele Piccolo, Francesco Burzotta, Fabrizio D’Ascenzo, Marco Agostino Deriu, Claudio Chiastra

“…Manual plaque assessment by experts is time-consuming, prone to errors, and affected by high inter-observer variability. To increase productivity, precision, and reproducibility, researchers are increasingly integrating artificial intelligence (AI)-based techniques into IVOCT analysis pipelines. …”
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A Hybrid Deep Learning–Based Approach for Visual Field Test Forecasting by Ashkan Abbasi, PhD, Sowjanya Gowrisankaran, PhD, Wei-Chun Lin, MD, PhD, Xubo Song, PhD, Bhavna Josephine Antony, PhD, Gadi Wollstein, MD, Joel S. Schuman, MD, Hiroshi Ishikawa, MD

“…Hybrid-VF-Net exhibited greater resilience to data reliability issues, particularly in managing high false-negative rates often seen in moderate-to-severe glaucoma cases due to increased test–retest variability. Additionally, it demonstrated improved performance with fewer prior VF tests, thus reducing the waiting time needed for progression analysis. …”
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Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields by Larona Keabetswe, Yiyin He, Chao Li, Zhenjiang Zhou

“…Three models were configured and compared for each CNN-RF (CNN-RF1, CNNRF2, CNNRF3) and CNN-SVM (CNN-SVM1, CNN-SVM2, CNN-SVM3), by using different combinations of variable input features derived from meteorological data (air temperature (Ta), vapour pressure deficit (VPD), net radiation (Rn)) and MODIS satellite data (land surface temperature (LST), fraction of photosynthetically active radiation (Fpar), leaf area index (LAI)). …”
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Enhancing Crop Health: Advanced Machine Learning Techniques for Prediction Disease in Palm Oil Tree by Nandy Manish, Kumar Yalakala Dinesh

“…This study builds predictive models by using a palmd database comprised of the large datasets of palm oil tree health indicators, environmental factors and historical disease outbreaks to identify early signs of disease with high accuracy.To analyze both structured as well as unstructured data multiple machine learning algorithms were used such as Random Forest, Support Vector Machines, Convolution Neural Networks. Environmental variables like temperatures, humidity and soil conditions; as well as features of the leaves, including their texture and shape were given as input features to the trained models. …”
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Two-Mode Hereditary Model of Solar Dynamo by Evgeny Kazakov, Gleb Vodinchar, Dmitrii Tverdyi

“…The feedback is represented by an integral term of the type of convolution of a quadratic form of phase variables with a kernel of a fairly general form. …”
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Evaluating soil erosion zones in the Kangsabati River basin using a stacking framework and SHAP model: a comparative study of machine learning approaches by Javed Mallick, Saeed Alqadhi, Swapan Talukdar, Md Nawaj Sarif, Tania Nasrin, Hazem Ghassan Abdo

“…The Boruta algorithm assessed the importance of these variables. Random Forest (RF), (Deep Neural Networks) DNN, Convolution Neural Network (CNN), and stacking (Meta model) models were used to map soil erosion susceptibility based on the inventory map and controlling features. …”
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cigChannel: a large-scale 3D seismic dataset with labeled paleochannels for advancing deep learning in seismic interpretation by G. Wang, G. Wang, G. Wang, X. Wu, X. Wu, X. Wu, W. Zhang, W. Zhang, W. Zhang

“…However, the synthetic seismic volumes in the <i>cigChannel</i> dataset still lack the variability and realism of field seismic data, potentially affecting the deep learning model's generalizability. …”
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Mechanism of Influence of Spatial Perception on Residents’ Emotion in Child-Friendly Urban Streets of Fuzhou City by Shaofeng CHEN, Zhengyan CHEN, Yuhan XU, Zheng DING

“…Future research should expand the diversity of data and refine sentiment recognition models to address cultural and environmental variability. By combining spatial indicators with emotional experiences, this research may contribute to the creation of inclusive, resilient and emotionally supportive child-friendly cities that prioritize safety and well-being.…”
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