Research on Credit Risk Prevention in Supply Chain Finance Driven by Blockchain by Lin Kejing

“…The article concludes by proposing innovative strategies based on blockchain technology, including constructing a credit information sharing platform, improving the credit assessment model, and establishing a risk early warning and monitoring system, with the aim of improving the efficiency and credit level of supply chain management and further promoting the healthy development of supply chain finance.…”
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Anchor Dragging Risk Estimation Strategy from Supervised Cost-Sensitive Learning by Sang-Lok Yoo, Shem Otoi Onyango, Joo-Sung Kim, Kwang-Il Kim

“…This study also demonstrated potential applications of the model, discussed its limitations, and suggested possible improvements for the ML approach. …”
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A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. …”
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HybridBranchNetV2: Towards reliable artificial intelligence in image classification using reinforcement learning. by Ebrahim Parcham, Mansoor Fateh, Vahid Abolghasemi

“…In particular, a 14% improvement obtained on the Visual Genome dataset and ImageNet 1K compared to the original HybridBranchNet model. …”
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Fed-DPSDG-WGAN: Differentially Private Synthetic Data Generation for Loan Default Prediction via Federated Wasserstein GAN by Padmaja Ramachandra, Santhi Vaithiyanathan

“…This paper uses federated learning to create synthetic data to improve loan defaulter prediction. By allowing multiple institutions to collaboratively train a shared model without directly exchanging sensitive loan data, this privacy-preserving approach allows for the utilization of more extensive and diverse datasets, potentially leading to significant improvements in prediction accuracy compared to traditional, siloed approaches. …”
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Study on the mechanisms associating community outdoor public spaces with elderly behavior by Lei Wang, Wenqi He, Shan Wu, Bo Zhang, Xiaorui Zhang, Hu Yin

“…This research delves into the intricate relationship between community outdoor public spaces and the behavioral patterns of the elderly, seeking to inform strategies for optimizing these spaces. The complexity and diversity of the mechanisms linking elderly behaviors with the characteristics of their outdoor environments pose challenges in identifying clear guidelines for improvement. …”
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Research on target localization and adaptive scrubbing of intelligent bathing assistance system by Ping Li, Ping Li, Shikai Feng, Hongliu Yu

“…The depth correction algorithm is designed to improve the depth accuracy of RGB-D vision sensors. …”
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XSShield: Defending Against Stored XSS Attacks Using LLM-Based Semantic Understanding by Yuan Zhou, Enze Wang, Wantong Yang, Wenlin Ge, Siyi Yang, Yibo Zhang, Wei Qu, Wei Xie

“…Experimental evaluation shows that XSShield achieves 93% accuracy and an F1 score of 0.9266 on the GPT-4 model, improving accuracy by an average of 88.8% compared to existing solutions. …”
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Deep learning-driven approach for cataract management: towards precise identification and predictive analytics by Shuaixin Lu, Lingling Ba, Jie Wang, Min Zhou, Peiyao Huang, Xiaohua Zhang, Simo Pan, Xinmiao Zhou, Kai Wen, Jing Sun

“…In the future, it is necessary to improve the generalization ability of model through multimodal data fusion, federated learning and other technologies, and combine interpretable design (such as Grad-CAM) to promote the evolution of DL to a transparent medical decision-making tool, and finally realize the intelligence and universality of cataract management.…”
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High-accuracy prediction of vessels’ estimated time of arrival in seaports: A hybrid machine learning approach by Sunny Md. Saber, Kya Zaw Thowai, Muhammad Asifur Rahman, Md. Mehedi Hassan, A.B.M. Mainul Bari, Asif Raihan

“…Compared to existing machine learning algorithms, our stacking model exhibits superior prediction performance. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. …”
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Slip and tractive efficiency of an electric tractor with a 4WID E-axle system by SeungYun Baek, HyeonHo Jeon, CheolGyu Park, YongJoo Kim

“…The highest tractive efficiency was observed when slip was within the 10–20% range, indicating that this slip range corresponds to the optimal operating condition. The primary findings of this study identify the appropriate slip range and provide fundamental data for developing slip control algorithms for the 4WID E-axle system. …”
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Non-Destructive Thickness Measurement of Energy Storage Electrodes via Terahertz Technology by Zhengxian Gao, Xiaoqing Jia, Jin Wang, Zhijun Zhou, Jianyong Wang, Dongshan Wei, Xuecou Tu, Lin Kang, Jian Chen, Dengzhi Chen, Peiheng Wu

“…Secondly, a hybrid signal processing algorithm is applied, combining an optimized Savitzky–Golay filter for high-frequency noise suppression with an enhanced sinc function wavelet threshold technique for signal fidelity improvement. …”
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A quantum random access memory (QRAM) using a polynomial encoding of binary strings by Priyanka Mukhopadhyay

“…In this paper we develop a new design for QRAM and implement it with Clifford+T circuit. We focus on optimizing the T-count and T-depth since non-Clifford gates are the most expensive to implement fault-tolerantly in most error correction schemes. …”
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DMF-YOLO: Dynamic Multi-Scale Feature Fusion Network-Driven Small Target Detection in UAV Aerial Images by Xiaojia Yan, Shiyan Sun, Huimin Zhu, Qingping Hu, Wenjian Ying, Yinglei Li

“…However, traditional detection models suffer significant performance degradation due to challenges including substantial scale variations, high proportions of small targets, and dense occlusions in UAV-captured images. …”
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STUDY OF ROBUST TOA DISCRIMINATORS FOR SPACE-BASED RADAR ALTIMETER by D. S. Borovitsky, A. E. Zhesterev, V. P. Ipatov, R. M. Mamchur

“…Besides, the threshold discriminators and simulation results are presented, as well as comparison of the robust  discriminators against  the  optimal (within the  classical model  framework) one.  …”
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Identification and validation of glucocorticoid receptor and programmed cell death-related genes in spinal cord injury using machine learning by Feng Lu, Yingying Liu, Zhen Chen, Shuning Chen, Weidong Liang, Fuzhou Hua, Maolin Zhong, Lifeng Wang

“…A total of 113 diagnostic models were developed through 12 machine learning algorithms, with the optimal model, “Lasso + Stepglm[both],” featuring six genes: Abca1, Cdh1, Glipr1, Glt8d2, Il10ra, and Pde5a. …”
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Remote monitoring of Tai Chi balance training interventions in older adults using wearable sensors and machine learning by Giulia Corniani, Stefano Sapienza, Gloria Vergara-Diaz, Andrea Valerio, Ashkan Vaziri, Paolo Bonato, Peter M. Wayne

“…Our framework comprises a model for identifying the specific Tai Chi movement being performed and a model to assess performance proficiency, both employing Random Forest algorithms and features from IMU signals. …”
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A Review of Vessel Time of Arrival Prediction on Waterway Networks: Current Trends, Open Issues, and Future Directions by Abdullah Al Noman, Aaron Heuermann, Stefan Wiesner, Klaus-Dieter Thoben

“…With the vast majority of global trade volume and value reliant on maritime transport, accurate prediction of vessel estimated time of arrival (ETA) is crucial for optimizing supply chain efficiency and managing logistical complexities in port operations. …”
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Integrating machine learning and reliability analysis: A novel approach to predicting heavy metal removal efficiency using biochar by Mohammad Sadegh Barkhordari, Chongchong Qi

“…This research introduces an advanced machine learning (ML) framework, utilizing deep forest (DF) algorithms, to predict and optimize the efficiency HM removal through biochar applications. …”
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