GCV-SLAM: Ground Constrained Visual SLAM Through Local Ground Planes by Yu Fan, Peng Zhang, Zhi Wang, Chengbao Liu, Guang Zhang

“…The classical visual Simultaneous Localization and Mapping(SLAM) algorithms assume that the camera moves in a free 3D space, while it is not valid for ground vehicles whose poses are influenced by ground surfaces. …”
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Edge Computing-Aided Dynamic Wireless Charging and Trip Planning of UAVs by Palwasha W. Shaikh, Hussein T. Mouftah

“…Simulations of this system demonstrated significant improvements: larger UAVs achieved an average charging efficiency of 91.2%, while smaller UAVs achieved 92.75%, with dynamic arrival management reducing wait times by an average of 1.5 min for smaller UAVs and 5.0 min for larger UAVs. …”
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HASSDE-NAS: Heuristic–Adaptive Spectral–Spatial Neural Architecture Search with Dynamic Cell Evolution for Hyperspectral Water Body Identification by Feng Chen, Baishun Su, Zongpu Jia

“…Additionally, a bidirectional fusion alignment cell jointly optimizes spectral and spatial dependencies. A heuristic cell search algorithm optimizes the network architecture through architecture stability, feature diversity, and gradient sensitivity analysis, which improves search efficiency and model robustness. …”
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A Beyond 5G OTFS Waveform Radio for Smart Hospital: PAPR Reduction in OTFS Using the Cuckoo Search–Based PTS Method by Arun Kumar, Aziz Nanthaamornphong

“…The results revealed that the proposed PTS + CS obtained significant PAPR and PSD performance while retaining the BER of the framework. The optimal PAPR, BER, PSD, and power-saving performance of the advanced radio enhance the quality of the B5G-based intelligent healthcare system.…”
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Experimental Study on the Unsteady Characteristics and the Impact Performance of a High-Pressure Submerged Cavitation Jet by Yongfei Yang, Wei Li, Weidong Shi, Ling Zhou, Wenquan Zhang

“…Optimizing the stand-off distance and the outlet geometry of the nozzles is found to be a good way to improve the performance of the cavitation jet.…”
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Quantum Edge Detection and Convolution Using Paired Transform-Based Image Representation by Artyom Grigoryan, Alexis Gomez, Sos Agaian, Karen Panetta

“…We demonstrate that our approach achieves improved processing speed compared to classical methods while maintaining comparable accuracy. …”
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G-RCenterNet: Reinforced CenterNet for Robotic Arm Grasp Detection by Jimeng Bai, Guohua Cao

“…Additionally, the GSConv module is incorporated during the prediction decoding phase to accelerate inference speed while maintaining high accuracy, further improving real-time performance. …”
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HSI Reconstruction: A Spectral Transformer With Tensor Decomposition and Dynamic Convolution by Le Sun, Xihan Ma, Xinyu Wang, Qiao Chen, Zebin Wu

“…Conversely, traditional iterative optimization algorithms are built upon sound mathematical derivations. …”
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Task-Driven Real-World Super-Resolution of Document Scans by Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel, Aleksandra Krzywon, Michal Kawulok

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The Adversarial Robust and Generalizable Stereo Matching for Infrared Binocular Based on Deep Learning by Bowen Liu, Jiawei Ji, Cancan Tao, Jujiu Li, Yingxun Wang

“…Our approach substantially improves adversarial robustness while maintaining accuracy on comparison with state-of-the-art methods which decrease nearly a half in EPE for quantitative results on widely used autonomous driving datasets. …”
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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…However, their reduced compressive strength (CS) due to the inclusion of rubber remains a significant barrier to widespread adoption. While several experimental studies exist, there is a clear gap in utilizing data-driven strategies to efficiently predict and optimize the strength performance of such materials. …”
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Know your competitor! Analyzing and predicting the location of competing stores: The case study of Valora at Swiss railway stations by Thomas Wieland

“…Binary logit models are built for the formats examined in order to identify the determinants of location choice. Machine learning algorithms are used to check and optimize the predictive ability of the models. …”
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Predictive framework of vegetation resistance in channel flow by Fengcong Jia, Weijie Wang, Yu Han, Jiayu Du, Yue Zhang, Zihan Liu, Hairong Gao

“…This study introduces a machine learning-based framework for predicting vegetation flow resistance, incorporating nine ML methods, including SVM, XGBoost, and BP. To improve predictive performance, optimization algorithms such as PSO, WSO, and RIME were applied. …”
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Unraveling the Reshaping of Human Resource Management Function: the mediating role of artificial intelligence by Samuel Bangura, Anos Chitamba Chitamba, Lucia Zithobile Ngidi, Sizwe Mbona

“…The results indicate that AI supports HRM professionals in executing tasks more efficiently and facilitates data analysis and process optimization. Additionally, the findings suggest that AI contributes to streamlining HR processes and has a positive impact on work-life balance, demonstrating that automation can improve both operational efficiency and employee well-being. …”
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Detection of kidney bean leaf spot disease based on a hybrid deep learning model by Yiwei Wang, Qianyu Wang, Yue Su, Binghan Jing, Meichen Feng

“…However, traditional machine learning methods face limitations in feature extraction, while deep learning approaches, despite their advantages, are computationally expensive and do not always yield optimal results. …”
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A Summary of the Existing Data on Cleft Surgical Outcomes: What Do We Not Know? by Priyanka Naidu, MBChB, MS, Alexander T. Plonkowski, MBBS, MRes, Caroline A. Yao, MD, MS, William P. Magee, III, MD, DDS

“…These limitations highlight the need for further research with more representative populations globally, standardized measurement tools, and a global consortium of cleft surgeons to make recommendations based on improved data. As the need for training in cleft surgery expands across the globe, evidence-based algorithms are essential to optimize outcomes and limit costly complications.…”
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Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control by Ebunle Akupan Rene, Willy Stephen Tounsi Fokui

“…This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints, thereby improving stability and performance under dynamic conditions. …”
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Robust Cross-Validation of Predictive Models Used in Credit Default Risk by Jose Vicente Alonso, Lorenzo Escot

“…Model validation is a challenging Machine Learning task, usually more difficult for consumer credit default models because of the availability of small datasets, the modeling of low-frequency events (imbalanced data), and the bias in the explanatory variables induced by the train/test sets split of the validation techniques (covariate shift). While many methodologies have been developed, cross-validation is perhaps the most widely accepted, often being part of the model development process by optimizing the hyperparameters of predictive algorithms. …”
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DQN-Based Shaped Reward Function Mold for UAV Emergency Communication by Chenhao Ye, Wei Zhu, Shiluo Guo, Jinyin Bai

“…By refining the DQN algorithm and devising a reward structure grounded on potential functions, we observe marked improvements in the final evaluation metrics, substantiating the efficacy of our approach. …”
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Hybrid feature selection for real-time road surface classification on low-end hardware: A machine learning approach by Cong Ngo Van, Duc-Nghia Tran, Ton That Long, Nguyen Gia Minh Thao, Duc-Tan Tran

“…The proposed feature selection approach significantly improves model efficiency and execution speed while maintaining high accuracy. …”
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