A construction method of a multidimensional and multilingual association network for earth surface system science data by Qinjun Qiu, Mengqi Hao, Liufeng Tao, Jiandong Liu, Weijie Li, Yang Wang, Zhong Xie

“…Enhancing the efficiency and adaptability of association algorithms is crucial for further advancements in this field. …”
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Research on Lightweighting Methods for 3D Building Models Based on Semantic Constraints by J. Wu, T. Shen, L. Huo

“…In the transition from LOD2 to LOD1, a concave-convex face movement and merging algorithm is utilized to reduce redundant data while maintaining geometric similarity and optimizing the model's topological structure, ultimately producing a lightweight LOD1 model. …”
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Wide-Angle Image Distortion Correction and Embedded Stitching System Design Based on Swin Transformer by Shiwen Lai, Zuling Cheng, Wencui Zhang, Maowei Chen

“…The model integrates multi-scale feature extraction, Thin Plate Spline (TPS) control point prediction, and optical flow-guided constraints, balancing correction accuracy and computational efficiency. Experiments on synthetic and real-world datasets show that the method outperforms mainstream algorithms, with PSNR gains of 3.28 dB and 2.18 dB on wide-angle and fisheye images, respectively, while maintaining real-time performance. …”
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The synergy of neuromarketing and artificial intelligence: A comprehensive literature review in the last decade by Ahmed H. Alsharif, Junhai Wang, Salmi Mohd Isa, Nor Zafir Md Salleh, Husam Azzawi Dawas, Mohammed H. Alsharif

“…Emotion, attention, and memory have become vital in NM and AI studies. AI algorithms analyze vast neural and physiological datasets, offering marketers insights into the emotional impact of campaigns, granular insights into consumer focus, and optimizing content for maximum impact. …”
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Two-Way Feature Extraction Using Sequential and Multimodal Approach for Hateful Meme Classification by Apeksha Aggarwal, Vibhav Sharma, Anshul Trivedi, Mayank Yadav, Chirag Agrawal, Dilbag Singh, Vipul Mishra, Hassène Gritli

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Design and performance analysis of wear-resistant micro-groove cutting tool: Numerical simulation and experimental study by Feilong Du, Fukuan Chen, Tao Zhou, Hongfei Yao, Xuefeng Zhao, Dongwei Zhu, Lin He

“…This investigation establishes a novel computational framework integrating finite element modeling (FEM), machine learning (ML)-based predictive analysis, and multi-objective genetic algorithm (GA) optimization for micro-groove tool design. …”
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HAF-YOLO: Dynamic Feature Aggregation Network for Object Detection in Remote-Sensing Images by Pengfei Zhang, Jian Liu, Jianqiang Zhang, Yiping Liu, Jiahao Shi

“…HAF-YOLO also maintained a low computational cost of 11.8 GFLOPs, outperforming other YOLO models. …”
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Deep convolutional neural network (DCNN)-based model for pneumonia detection using chest x-ray images by S. I. Ele, U. R. Alo, H. F. Nweke, A. H. Okemiri, E. O. Uche-Nwachi

“…This study focuses on developing and implementing a machine learning model tailored specifically for medical diagnosis, leveraging advancements in computer vision and deep learning algorithms. This research aims to design an efficient and accurate model capable of classifying medical images into distinct categories, enabling automated diagnosis and identification of various ailments and conditions. …”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…To address these limitations, the integration of digital technologies, such as computational modeling and machine learning (ML), has become increasingly important.MethodsThis paper proposes a hybrid multiscale modeling framework that integrates molecular dynamics (MD) simulations, finite element methods (FEM) from continuum mechanics, and supervised ML algorithms—including deep neural networks and gradient boosting regressors—to enable accurate and efficient prediction of material properties across scales. …”
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Evaluation of Feature Transformation and Machine Learning Models on Early Detection of Diabetes Mellitus by Ahmed Ali Linkon, Inshad Rahman Noman, Md Rashedul Islam, Joy Chakra Bortty, Kanchon Kumar Bishnu, Araf Islam, Rakibul Hasan, Masuk Abdullah

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High-Fidelity Universal Gates in the ^{171}Yb Ground-State Nuclear-Spin Qubit by J. A. Muniz, M. Stone, D. T. Stack, M. Jaffe, J. M. Kindem, L. Wadleigh, E. Zalys-Geller, X. Zhang, C.-A. Chen, M. A. Norcia, J. Epstein, E. Halperin, F. Hummel, T. Wilkason, M. Li, K. Barnes, P. Battaglino, T. C. Bohdanowicz, G. Booth, A. Brown, M. O. Brown, W. B. Cairncross, K. Cassella, R. Coxe, D. Crow, M. Feldkamp, C. Griger, A. Heinz, A. M. W. Jones, H. Kim, J. King, K. Kotru, J. Lauigan, J. Marjanovic, E. Megidish, M. Meredith, M. McDonald, R. Morshead, S. Narayanaswami, C. Nishiguchi, T. Paule, K. A. Pawlak, K. L. Pudenz, D. Rodríguez Pérez, A. Ryou, J. Simon, A. Smull, M. Urbanek, R. J. M. van de Veerdonk, Z. Vendeiro, T.-Y. Wu, X. Xie, B. J. Bloom

“…Arrays of optically trapped neutral atoms are a promising architecture for the realization of quantum computers. In order to run increasingly complex algorithms, it is advantageous to demonstrate high-fidelity and flexible gates between long-lived and highly coherent qubit states. …”
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An enhanced pivot-based neural machine translation for low-resource languages by Danang Arbian Sulistyo, Aji Prasetya Wibawa, Didik Dwi Prasetya, Fadhli Almuíini Ahda

“…The data collecting approach entailed extracting parallel texts from internet sources, followed by pre-processing through tokenization, normalization, and stop-word elimination algorithms. The prepared datasets were utilized to train and assess the NMT models. …”
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Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer by Le Zhang, Chunqiu Zheng, Tian Li, Lei Xing, Han Zeng, Tingting Li, Huan Yang, Jia Cao, Badong Chen, Ziyuan Zhou

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Fraud Detection in Privacy Preserving Health Insurance System Using Blockchain Technology by Md. Mazharul Islam, Mubasshir Ahmed, Rajesh Palit, Mohammad Shahriar Rahman, Salekul Islam

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Sample Training Based Wildfire Segmentation by 2D Histogram θ-Division with Minimum Error by Jianhui Zhao, Erqian Dong, Mingui Sun, Wenyan Jia, Dengyi Zhang, Zhiyong Yuan

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Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis by Emre Bilgin

“…Predictive algorithms help stratify and predict disease outcomes, while clinical decision support systems integrate clinical and imaging data for optimized management. …”
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Application of LiDAR and SLAM Technologies in Autonomous Systems for Precision Grapevine Pruning and Harvesting by Obaid Saif, Mohammed G., Nagaraju Krishna Chythanya

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From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications by Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou, Constantinos Halkiopoulos

“…High computational cost is prohibitive to the use of deep learning models in real-world applications, therefore indicating a need for the development and application of optimization techniques. …”
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The Role of Artificial Intelligence in Sports Analytics: A Systematic Review and Meta-Analysis of Performance Trends by Przemysław Pietraszewski, Artur Terbalyan, Robert Roczniok, Adam Maszczyk, Kajetan Ornowski, Daria Manilewska, Szymon Kuliś, Adam Zając, Artur Gołaś

“…Sixteen peer-reviewed studies spanning 13 distinct sports disciplines were included, employing a variety of AI techniques—from classical machine learning algorithms to advanced deep learning and computer vision models. …”
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Reconstructing the Spectrum of a Polyharmonic Signal under Slow Fluctuations in the Sampling Period by A. A. Monakov

“…Methods for reconstructing the spectrum of a signal, the sampling moments of which are unknown a priori, are based on a preliminary reconstruction of the signal and subsequent estimation of its spectrum. Existing algorithms for reconstructing a signal sampled on an uneven time grid with unknown nodes are characterized by a high computational complexity due to their iterative nature and reliance on optimization search methods.Aim. …”
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