Novel data visualization and risk analytic algorithm in a low-middle income country by Hina Inam, Fatima Amjad, Abdul Ahad Sohail, Qalab Abbas

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Optimization model for reliability-based maintenance strategies of critical components in electric multiple units by CHEN Jiayu, SONG Dongli, GENG Fangdong

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Cutting-Edge Phishing Detection Using Novel Features and Hybrid Machine Learning Techniques by Jin Zhang

“…According to the trial data, the LGLO hybrid model outperformed other algorithms. In the accuracy value at the train section, the LGLO model with the value of (0.911) has the best performance compared to the LGRS and LGPD models.…”
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SIMPLE ALGORITHM TO CONSTRUCT CIRCULAR CONFIDENCE REGIONS IN CORRESPONDENCE ANALYSIS USING R by Karunia Eka Lestari, Marsah Rahmawati Utami, Mokhammad Ridwan Yudhanegara

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FArSide Trained Active Region Recognition (FASTARR): A Machine Learning Approach by Amr Hamada, Mitchell Creelman, Kiran Jain, Charles Lindsey

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When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges by Chao Wang, Jiaxuan Zhao, Licheng Jiao, Lingling Li, Fang Liu, Shuyuan Yang

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Skill decay following Basic Life Support training: a systematic review protocol by Andrew Owen, Joseph Alderman, Emily Beesley, Jonathan Hulme, Benjamin Stanley, Thomas Burton, Harriet Percival, Nicholas Coffin

“…The aim of this systematic review is to summarise the literature regarding skill decay following BLS training, reporting particularly the time period over which this occurs, and which components of would-be rescuers’ performance of the BLS algorithm are most affected.Methods and analysis A search will be conducted to identify studies in which individuals have received BLS training and received subsequent assessment of their skills at a later date. …”
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Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models? by P. Sun, P. B. Holden, H. J. B. Birks, H. J. B. Birks

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The European regulation on AI in the face of the biases and risks of algorithmic discrimination in migration contexts by Encarnación La Spina

“…While global migration contexts face tight security control and an extensive digital transition, the European regulation on AI is still too limited in scope to counteract the negative impacts of the design and training of algorithms. With that in mind, this paper provides a critical review of how AI-driven systems are introduced into European migration control and management, and analyses why the new legal framework for AI does not do enough to prevent the discriminatory impact of biases, stereotypes and risks associated with the asymmetric power dynamics and the structures of social injustice underlying migration.…”
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GAN-based pseudo random number generation optimized through genetic algorithms by Xuguang Wu, Yiliang Han, Minqing Zhang, Yu Li, Su Cui

“…In this paper, we present a Genetic Algorithm Optimized Generative Adversarial Network (hereinafter referred to as GAGAN), which is designed for the effective training of discrete generative adversarial networks. …”
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A Comparative Study of Data-Driven Prognostic Approaches under Training Data Deficiency by Jinwoo Song, Seong Hee Cho, Seokgoo Kim, Jongwhoa Na, Joo-Ho Choi

“…While the data-driven approach is the most common for this purpose, they often face challenges due to insufficient training data. This study delves into the prognostic capabilities of four methods under the conditions of limited training datasets. …”
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Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design by Paul Adunola, Luis Felipe V. Ferrão, Juliana Benevenuto, Camila F. Azevedo, Patricio R. Munoz

“…Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. …”
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TIBW: Task-Independent Backdoor Watermarking with Fine-Tuning Resilience for Pre-Trained Language Models by Weichuan Mo, Kongyang Chen, Yatie Xiao

“…This paper presents a novel watermarking scheme, TIBW (Task-Independent Backdoor Watermarking), that embeds robust, task-independent backdoor watermarks into pre-trained language models. By implementing a Trigger–Target Word Pair Search Algorithm that selects trigger–target word pairs with maximal semantic dissimilarity, our approach ensures that the watermark remains effective even after extensive fine-tuning. …”
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Detection of Abnormal Pedestrian Flows with Automatic Contextualization Using Pre-Trained YOLO11n by Adrián Núñez-Vieyra, Juan C. Olivares-Rojas, Rogelio Ferreira-Escutia, Arturo Méndez-Patiño, José A. Gutiérrez-Gnecchi, Enrique Reyes-Archundia

“…A strategy to measure the accuracy of the data generated for such contextualization is also proposed. The pre-trained YOLO11n algorithm and the Bot-SORT algorithm gave the best results in person detection and tracking, respectively.…”
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Research on Risk Identification of Coal Mine Ventilation Systems Based on HFACS and Apriori Algorithm by Mingjia Jing, Guoxing Zhang, Dongjie Yang, Hongli Qin

“…This paper collects data from 138 coal mine ventilation accident reports, employs the human factors analysis and classification system (HFACS) and the Apriori association rule algorithm, and utilizes Gephi for visualization of the association rules, in order to reveal the relationships among the causes of ventilation accidents. …”
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Research on an electro-mechanical braking control method for rail trains based on braking force control by WEN Tao, ZHUANG Wenjie, YAN Minjie, LIU Kai, YUAN Wenye

“…Additionally, the effectiveness of the control algorithm is verified.…”
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Passenger Demand-Oriented High-Speed Train Stop Planning with Service-Node Features Analysis by Fanxiao Liu, Qiyuan Peng, Gongyuan Lu, Guangyuan Zhang

“…As a critical foundation for train traffic management, a train stop plan is associated with several other plans in high-speed railway train operation strategies. …”
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Dim and Small Target Detection Based on Local Feature Prior and Tensor Train Nuclear Norm by Anqing Wu, Xiangsuo Fan, Lei Min, Wenlin Qin, Ling Yu

“…Secondly, we propose a multi-frame density peak search algorithm to obtain local feature information by combining the features associated with multiple contiguous frames of the target. …”
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Exploring immune-inflammation markers in psoriasis prediction using advanced machine learning algorithms by Li Yang, Shixin He, Li Tang, Xiao Qin, Yan Zheng

“…Subsequently, nine classification algorithms were developed using the processed training set, including random forest, neural networks, XGBoost, k-nearest neighbors, gradient boosting, logistic regression, naïve Bayes, AdaBoost, and SVMs. …”
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Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…The YOLOv5 (you only look once) method is designed to rapidly and accurately detect specific target objects and their locations in images after training on a sampled dataset. The YOLOv5 algorithm adopted in this study excels at detecting small targets and provides multi-scale detection, strong versatility, fast training, inference speeds, and adaptable fine-tuning capabilities. …”
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