A Comparative Study of Deep Reinforcement Learning Algorithms for Urban Autonomous Driving: Addressing the Geographic and Regulatory Challenges in CARLA by Yechan Park, Woomin Jun, Sungjin Lee

“…To enable autonomous driving in real-world environments that involve a diverse range of geographic variations and complex traffic regulations, it is essential to investigate Deep Reinforcement Learning (DRL) algorithms capable of policy learning in high-dimensional environments characterized by intricate state–action interactions. …”
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Enhancing User Experience in Interactive Virtual Museums for Cultural Heritage Learning Through Extended Reality: The Case of Sanxingdui Bronzes by Xin Huang, Yuzhi Li, Feng Tian

“…However, traditional virtual museums are often limited to simple navigation and display functions, lacking in-depth interaction and struggling to provide personalized learning experiences. …”
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Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms. by Ayşenur Eser, Sinem Burcu Erdoğan

“…Automated detection of emotional states through brain-computer interfaces (BCIs) offers significant potential for enhancing user experiences and personalizing services across domains such as mental health, adaptive learning and interactive entertainment. Within this advancing field, the aim of this study was to test the feasibility of a functional near-infrared spectroscopy (fNIRS)-based BCI system for accurate prediction and objective identification of three fundamental emotional states that involved positive, negative and neutral conditions. …”
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FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection by Zhijie Li, Jiahui Zhang, Yingjie Zhang, Dawei Yan, Xing Zhang, Marcin Woźniak, Wei Dong

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Cheby-KANs: Advanced Kolmogorov–Arnold Networks for Applying Geometric Deep Learning in Quantum Chemistry Applications by Amgad Abdallah Mahmoud, Andreas Pester, Maha M. Muttardi, Frederic Andres, Shihori Tanabe, Nicolas Greneche, Hesham H. Ali

“…We integrated our algorithm with a geometric deep learning model (SchNet) to assess the effect of integrating KANs representation on geometric deep learning by combining domain knowledge of quantum chemistry and our new representation. …”
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Assessing relative contributions of climate and socio-environmental factors to ecosystem health through space–time interpretable machine learning by Xiaoliang Meng, Junyi Wu, Yichun Xie, Yongfei Bai, Chenghu Zhou, Yuchen Li

“…The biggest challenge of sustainability is improving ecosystem production services while preserving other functions at large scales. One solution is to seek a delicate equilibrium from complex nonlinear trade-offs between socioeconomic development, climate change, and environmental protection to maximize ecosystem services. …”
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Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning by Caterina Barrasso, Robert Krüger, Anette Eltner, Anna F. Cord

“…The decline of segetal flora species across Europe, driven by intensified agricultural practices, is impacting other taxa and ecosystem functions. Result-based payments to farmers offer an effective solution to conserve these species, but the high cost of biodiversity monitoring remains a challenge. …”
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Training and inference Time Efficiency Assessment Framework for machine learning algorithms: A case study for hyperspectral image classification by Xiaorou Zheng, Jianxin Jia, Shoubin Dong, Yawei Wang, Runuo Lu, Yuwei Chen, Yueming Wang

“…To address these limitations, we propose the Time Efficiency Assessment Framework (TEAF), a novel method for evaluating the time efficiency of machine learning algorithms. Through mathematical reasoning, TEAF models the training and inference time as functions (ψ) of complex data scales and core model parameters. …”
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Integrated Analysis of Ferroptosis- and Cellular Senescence-Related Biomarkers in Atherosclerosis Based on Machine Learning and Single-Cell Sequencing Data by Qi X, Cao S, Chen J, Yin X

“…Eight machine learning algorithms were applied to identify hub genes and construct a diagnostic model. …”
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Learning from the process evaluation of a complex, pre-conception randomised controlled trial in Malaysia: the Jom Mama project by Shakirah Md. Sharif, Mark Hanson, Diane W. Chong, Ainul Nadziha M. Hanafiah, Mohamad Z. Zulkepli, Syatirah F. Zulbahari, Jutta Skau, Julius Cheah C. Ho, Priya Matzen, Regien Biesma, Shane A. Norris, Jens Aagaard-Hansen

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Leveraging assistive technology for visually impaired people through optimal deep transfer learning based object detection model by Mahir Mohammed Sharif Adam, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Samah Al Zanin

“…Abstract Visual impairment, such as blindness, can have a profound impact on an individual’s cognitive and psychological functioning. Therefore, the use of assistive techniques can help alleviate the adverse effects and enhance the quality of life for people who are blind. …”
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Advancing higher education on sustainable land use: designing socioscientific inquiry-based learning units on oil palm cultivation by Finn Kristen Matthiesen, Jacqueline Dischereit, Raissa Mataniari, Fitta Setiajiati, Fitta Setiajiati, I Nengah Suparta, Susanne Bögeholz, Susanne Bögeholz

“…The land-use research addressed in this DBR is based on the international Collaborative Research Centre 990 “Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems” (EFForTS). …”
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Data enhanced iterative few-sample learning algorithm-based inverse design of 2D programmable chiral metamaterials by Zhao Zeyu, You Jie, Zhang Jun, Du Shiyin, Tao Zilong, Tang Yuhua, Jiang Tian

“…Meanwhile, according to the impact of geometric parameters on the chiroptical response, a new functionality is added to interpret the experimental results of DEIFS algorithm from the perspective of data, improving the interpretability of the DEIFS. …”
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Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms by Hanqi Li, Jiayi Zhang, Yu Shi, Huanhuan Wang, Ruida Yang, Shaobo Wu, Yue Li, Xue Yang, Qingguang Liu, Liankang Sun

“…Distinct immune and mutation landscape characteristics were observed between patients categorized into high and low matrix stiffness groups. PPARG functioned in tumorigenesis through HSC activation and immune suppression. …”
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Deep learning–based classification of parotid gland tumors: integrating dynamic contrast-enhanced MRI for enhanced diagnostic accuracy by Kazim Ayberk Sinci, Ilker Ozgur Koska, Yusuf Kenan Cetinoglu, Nezahat Erdogan, Ali Murat Koc, Nuket Ozkavruk Eliyatkin, Cagan Koska, Barkan Candan

“…Conclusions Adding DCE-MRI data to deep learning models substantially enhances parotid gland tumor classification accuracy, highlighting the value of functional imaging biomarkers in improving noninvasive diagnostic workflows.…”
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Application of Machine Learning Models for the Early Detection of Metritis in Dairy Cows Based on Physiological, Behavioural and Milk Quality Indicators by Karina Džermeikaitė, Justina Krištolaitytė, Ramūnas Antanaitis

“…In this study, we investigated the potential of machine learning (ML) techniques applied to physiological, behavioural, and milk quality parameters for the early detection of metritis in dairy cows during the postpartum period. …”
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Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete by Xiaoyan Wang, Yantao Zhong, Fei Zhu, Jiandong Huang

“…Various MLP architectures are explored, evaluating transfer functions and training techniques to optimize performance. …”
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Machine learning model and hemoglobin to red cell distribution width ratio evaluates all-cause mortality in pulmonary embolism by Dan Du, Lei Zhang, Xue Wen, Xian-Ming Zhang, Ya-dong Yuan

“…Abstract The ratio of hemoglobin (Hb) to red blood cell distribution width (RDW), known as HRR, functions as an innovative indicator related to prognosis. …”
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Screening of CAD-related secretory genes associated with type II diabetes based on comprehensive bioinformatics analysis and machine learning by Li Xie, Han Xiao, Maoyu Zhao, Li Xu, Si Tang, Youzhu Qiu

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ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction by Dong Wang, Jieyu Jin, Guqin Shi, Jingxiao Bao, Zheng Wang, Shimeng Li, Peichen Pan, Dan Li, Yu Kang, Tingjun Hou

“…Abstract The Caco-2 cell model has been widely used to assess the intestinal permeability of drug candidates in vitro, owing to its morphological and functional similarity to human enterocytes. While Caco-2 cell assay is considered safe and cost-effective, it is also characterized by being time-consuming. …”
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