Impact of rainy season on approach trajectories in high-altitude airport terminal maneuvering area: a clustering analysis by Jianxiong Chen, Jingtao Wang, Fan Li, Lin Zou

“…With the widespread use of ADS-B data, flight monitoring has improved, providing detailed insights into aircraft trajectories. …”
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Video Detection and Fusion Tracking for the Targets in Traffic Scenario by Pan Li, Jianing Zhang, Chengxi Han, Luping Xu, Baoguo Feng, Yongjun Zhou, Sen Zhang, Bo Zhang

“…The experimental results demonstrate that the association algorithm for optimal data fusion proposed in this paper improves performance by 3.86% and 2.13% compared with the traditional fusion method and the IOU matching fusion, respectively. …”
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RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images by Yansong Wang, Xuanxi Yang, Haoyu Wang, Huihua Wang, Zaiqing Chen, Lijun Yun

“…In this paper, we propose a walnut detection method based on UAV (UAV means Unmanned Aerial Vehicle) remote sensing imagery to improve the walnut yield prediction accuracy. Based on the YOLOv11 network, we propose several improvements to enhance the multi-scale object detection capability while achieving a more lightweight model structure. …”
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HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot by Xiaoming Li, Yang Zhou, Yongguang Li, Shiqi Wang, Wenxue Bian, Hongmin Sun

“…Furthermore, the overall generalization ability of the model is improved through hyperparameter optimization based on the tree-structured Parzen estimator (TPE). …”
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Ship-DETR: A Transformer-Based Model for EfficientShip Detection in Complex Maritime Environments by Yi Wang, Xiang Li

“…Experimental results on the LEVIR-Ship dataset and the HRSC2016-MS dataset demonstrate that Ship-DETR improves the ship detection accuracy while maintaining a small number of parameters and computational load. …”
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…This study's novelty lies in directly comparing a diverse range of ML and DL algorithms while highlighting the potential of advanced computational approaches for renewable energy optimization.…”
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Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends by Seyed Mahdi Miraftabzadeh, Andrea Di Martino, Michela Longo, Dario Zaninelli

“…Future research will focus on self-healing grids, optimal power flow, and energy trading for improved reliability and security.…”
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Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review by Jie Zhang, Xinchun Zhu, Yigong Xie, Guo Chen, Shuangquan Liu

“…This review systematically examines recent advancements in the field, focusing on three principal areas: the definition and detection of ramp event characteristics, innovations in predictive model architectures, and strategies for precision optimization. Our analysis reveals that while detection algorithms for ramp events have matured and the overall predictive performance of power forecasting models has improved, existing approaches often struggle to capture localized ramp phenomena, resulting in persistent deviations. …”
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Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networks by Jian-Dong Yao, Wen-Bin Hao, Zhi-Gao Meng, Bo Xie, Jian-Hua Chen, Jia-Qi Wei

“…Our proposed MARL framework enables adaptive, decentralized decision-making for both the distribution system operator and individual VPPs, optimizing economic efficiency while maintaining grid stability. …”
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MAPE-ViT: multimodal scene understanding with novel wavelet-augmented Vision Transformer by Muhammad Waqas Ahmed, Touseef Sadiq, Hameedur Rahman, Sulaiman Abdullah Alateyah, Mohammed Alnusayri, Mohammed Alatiyyah, Dina Abdulaziz AlHammadi

“…The feature discrimination capability is further enhanced through optimization using the Gray Wolf algorithm. The processed features then flow into a dual-stream architecture, where an extreme learning machine handles multi-object classification, while conditional random fields (CRF) manage scene-level categorization. …”
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Deep learning-based detection and classification of acute lymphoblastic leukemia with explainable AI techniques by Debendra Muduli, Sourav Parija, Suhani Kumari, Asmaul Hassan, Harendra S. Jangwan, Abu Taha Zamani, Sk. Mohammed Gouse, Banshidhar Majhi, Nikhat Parveen

“…Additionally, we evaluated the performance of these models using different optimization techniques, including Adadelta, SGD, RMSprop, and Adam, to determine the most effective optimization strategy for improving classifica-tion accuracy. …”
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Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…Compared to existing superficial deposit maps, our peat predictions significantly improve the spatial detail of peatlands at the national level, offering new opportunities for land use planning and emission mitigation. …”
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Research on Viewpoints Planning for Industrial Robot-Based Three-Dimensional Sculpture Reconstruction by Zhen Zhang, Changcai Cui, Guanglin Qin, Hui Huang, Fangchen Yin

“…Subsequently, local viewpoint planning is conducted to refine regions that were incompletely captured in the initial step, with a genetic algorithm optimizing the scanning paths to enhance efficiency. …”
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Secure Beamforming via Amplify-and-Forward Relays in Machine-to-Machine Communications by Zhongjian Liu, Xiaoning Zhang, Lin Bai, Chen Chen, Haige Xiang

“…Simulation results are presented to demonstrate the significance of performance improvements with the SRM and RPM beamforming schemes. …”
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Degree-Constrained k-Minimum Spanning Tree Problem by Pablo Adasme, Ali Dehghan Firoozabadi

“…Our numerical results indicate that the proposed models and algorithms allow obtaining optimal and near-optimal solutions, respectively. …”
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A generative deep neural network for pan-digestive tract cancer survival analysis by Lekai Xu, Tianjun Lan, Yiqian Huang, Liansheng Wang, Junqi Lin, Xinpeng Song, Hui Tang, Haotian Cao, Hua Chai

“…Results In this study, we propose GDEC, a multi-task generative deep neural network designed for precise digestive tract cancer subtyping. The network optimization process involves employing an integrated loss function consisting of two modules: the generative-adversarial module facilitates spatial data distribution understanding for extracting high-quality information, while the clustering module aids in identifying disease subtypes. …”
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ENHANCING GRID INTEGRATION OF A SINGLE-PHASE SOLAR INVERTER THROUGH ADVANCED CONTROL TECHNIQUES AND REAL-TIME VALIDATION by Anupama Subhadarsini, Babita Panda, Byamakesh Nayak

“…The CMAES-CMBO algorithm optimizes the controller's parameters. Comparative analysis of the CMAES-CMBO-HFF-2DoF-FOTIDC controller with other state-of-the-art controllers demonstrates its superior performance and effectiveness. …”
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A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…XGBoost optimizes decision tree ensembles and provides class probabilities, while the ANN processes numerical data to learn deeper representations. …”
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A data driven approach to urban area delineation using multi source geospatial data by Chenyu Fang, Lin Zhou, Xinyue Gu, Xing Liu, Martin Werner

“…Abstract This study introduces a data-driven, bottom-up approach to urban delineation, integrating feature engineering with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which represents a significant improvement in precision and methodology compared to traditional approaches that rely on simplistic OpenStreetMap (OSM) road node data aggregations. …”
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Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping by Oluwatobi Emmanuel Dare, Kennedy Okokpujie, Emmanuel Adetiba, Olabode Idowu-Bismark, Abdultaofeek Abayomi, Raymond Jules Kala, Emmanuel Owolabi, Udeme Christopher Ukpong

“…In this study, we utilised conditional generative adversarial network (CGAN) to create a television spectrum radio environment map in order to improve cognitive television white space (TVWS) radio performance in real-time propagation environments. …”
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