ClassRoom-Crowd: A Comprehensive Dataset for Classroom Crowd Counting and Cross-Domain Baseline Analysis by Wenqian Jiang, Xiaohua Huang, Qun Zhao, Sheng Liu

“…However, there is a lack of specialized datasets tailored to specific educational scenarios and extreme conditions. Furthermore, to enhance crowd movement predictions in real-world applications, these methods must demonstrate temporal coherence. …”
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Quality monitoring of hybrid welding processes: A comprehensive review by Solomon Habtamu Tessema, Dariusz Bismor

“…Furthermore, the review highlights the integration of sensor data with AI/ML algorithms and digital twin technologies, enhancing the capabilities of quality monitoring systems. …”
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Effective tweets classification for disaster crisis based on ensemble of classifiers by Christopher Ifeanyi Eke, Kholoud Maswadi, Musa Phiri, Mulenga, Mohammad Imran, Dekera Kwaghtyo, Akeremale Olusola Collins

“…The proposed approach demonstrates not only high predictive capacity but also scalability for real-time tweet filtering during emergencies. …”
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Clinical Utility of Bladder Cancer Biomarkers by Laura Maria Krabbe, Georgios Gakis, Yair Lotan

“…The need for frequent follow-up, including invasive procedures like cystoscopy, repetitive procedures like transurethral resection of bladder tumors and intravesical instillation therapy in non-muscle invasive stages, as well as systemic treatment with or without radical local treatment in advanced stages, makes bladder cancer one of the most expensive cancers to treat. Prognostic and predictive biomarkers have the potential to fundamentally change bladder cancer treatment algorithms, which may result in improved patient comfort and oncological outcomes and may also decrease the socioeconomic burden of the disease. …”
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The Fractality of Sentiment Arcs for Literary Quality Assessment: the Case of Nobel Laureates by Yuri Bizzoni, Pascale Moreira, Mads Rosendahl Thomsen, Kristoffer L. Nielbo

“…To test this hypothesis, we present the results from two studies: (i) a probability distribution test, where we compute the probability of seeing a title from a Nobel laureate at different levels of arc fractality; (ii) a classification test, where we use several machine learning algorithms to measure the predictive power of both sentiment arcs and their fractality measure. …”
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Research on the optimization model of anti-breast cancer candidate drugs based on machine learning by Zhou Dong, Hong Chen, Yuchen Yang, Hairong Hao

“…The constructed Quantitative Structure-Activity Relationship (QSAR) model, using algorithms such as LightGBM, Random Forest, and XGBoost, achieved an R2 value of 0.743 for biological activity prediction, demonstrating strong predictive performance. …”
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Delineating flood susceptibility zones using novel ensemble models – An application of evidential belief function, relative frequency ratio, and Shannon entropy by Samuel Yaw Danso, Yi Ma, Isaac Yeboah Addo

“…Three bivariate statistical algorithms, namely evidential belief function (EBF), relative frequency ratio (RFR), and Shannon entropy (SE) were combined through basic arithmetic operations to produce nine ensemble scenarios. …”
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Application of a Hybrid Model for Data Analysis in Hydroponic Systems by Kuanysh Bakirov, Jamalbek Tussupov, Akhmet Tussupov, Ibraheem Shayea, Aruzhan Shoman

“…Experimental evaluation demonstrated superior predictive performance by using XGBoost, achieving an accuracy and F1-score of 97.88%, ROC-AUC of 99.99%, and computational efficiency (training completed in 2.3 s), outperforming RandomForest and GradientBoosting algorithms. …”
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Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers by Raja Azlina Raja Mahmood, AmirHossien Abdi, Masnida Hussin

“…These evolutionary-based algorithms are known to be effective in solving optimization problems.  …”
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A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment by Mohammad Hossein Zadeh, Marina Barbiroli, Franco Fuschini

“…The proposed model relies on a combination of predictive algorithms, including a linear regression model and a Multi-Layer Perceptron, working collaboratively to model the relationship between the considered propagation markers and input features like frequency and machine size, spacing, and density. …”
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Aligned in Human Dignity? Parallel Anthropological Aspects of EU Tech Regulation and Pope Francis’ Teaching on AI by Bernát Török, Ádám Darabos

“…We will highlight four concrete challenges posed by artificial intelligence to the human person (profiling, predictive algorithms, human intervention and manipulation), to which the legislation of the European Union has responded partly on anthropological grounds. …”
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Intelligent design of Fe–Cr–Ni–Al/Ti multi-principal element alloys based on machine learning by Kang Xu, Zhengming Sun, Jian Tu, Wenwang Wu, Huihui Yang

“…This study proposes an innovative intelligent optimization algorithm (OA) to refine feature selection in machine learning (ML) models, targeting the prediction of ultimate tensile strength (UTS) and fracture elongation (FE) in MPEAs. …”
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Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model. by Elias Vera-Siguenza, Cristina Escribano-Gonzalez, Irene Serrano-Gonzalo, Kattri-Liis Eskla, Fabian Spill, Daniel Tennant

“…To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. …”
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Machine Learning Unveils the Impacts of Key Elements and Their Interaction on the Ambient-Temperature Tensile Properties of Cast Titanium Aluminides Employing SHAP Analysis by Shiqiu Liu, Li Liang

“…Comparative analysis of three algorithms within the training dataset proved the random forest regression (RFR) as the optimal modeling approach. …”
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Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer by Zihao Bai, Hao Wang, Jingru Han, Jia An, Zhaocong Yang, Xuming Mo

“…This study applied multiomics approaches, integrating transcriptomic, epigenetic, and somatic mutation data, with consensus clustering algorithms to classify GC molecular subtypes and assess their biological and immunological features. …”
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Exosomal microRNA signatures in youth at clinical high risk for bipolar disorder by Xinyu Meng, Shengmin Zhang, Yingzhen Xu, Zaohui Ma, Shuzhe Zhou, Yantao Ma, Hong Ma, Xin Yu, Lili Guan

“…Using machine-learning algorithms, target miRNAs were selected from differentially expressed candidates. …”
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Artificial Intelligence Precision Recognition and Auxiliary Diagnosis of Dental X-ray Panoramic Images Based on Deep Learning by Liu Riming, Gao Zhenshan

“…Additionally, visualization methods were used to display the model’s prediction results across different lesion scales (small caries, medium caries, large caries). …”
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Constitutional-Legal Aspect of Creating Large Language Models: the Problem of Digital Inequality and Linguistic Discrimination by I. G. Ilin

“…While direct forms of discrimination can be detected in predictive algorithms, generative models create more subtle but no less significant cumulative effects. …”
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Applications of Artificial Intelligence in Vasculitides: A Systematic Review by Mahmud Omar, Reem Agbareia, Mohammad E. Naffaa, Abdulla Watad, Benjamin S. Glicksberg, Girish N. Nadkarni, Eyal Klang

“…Conclusion The current literature shows that AI algorithms can enhance vasculitis diagnosis and prediction, with deep‐ and machine‐learning models showing promise in Kawasaki disease. …”
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Matrix Metalloproteinase-9 is associated with tumor microenvironment remodeling of bladder cancer by Fang Fang, Tiange Wu, Mengxue Wang, Wenchao Li, Zonghao You, Ming Chen, Han Guan

“…Matrix Metalloproteinase-9 (MMP9) was identified as a predictive biomarker related to immune microenvironment. …”
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