Artificial Intelligence in Forensic Expertology by E. V. Chesnokova, A. I. Usov, G. G. Omel’yanyuk, M. V. Nikulina

“…The authors have developed the criteria for choosing the direction for the introduction of AI in the system of forensic expertology, and specifically in its innovative section – the management system of forensic expert activity.The prospects for the development of AI technologies in forensic examination are associated with the processing of big data based on the expert situation, the reliability of the data used in AI training and further validation (assessment of suitability) of the applied training methodology, analysis of the results of AI technology. …”
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The Use of Artificial Intelligence in Medical Diagnostics: Opportunities, Prospects and Risks by Nataliia Sheliemina

“…However, the AI use in medicine carries certain risks related to ethics and data privacy, shortcomings in the quality of data for training algorithms, and importance of protecting against cyberthreats. …”
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Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression by Kun Guo, Kun Guo, Bo Zhu, Lei Zha, Yuan Shao, Zhiqin Liu, Naibing Gu, Kongbo Chen

“…Model performance was evaluated using the Area Under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, predictive values, and F1 score, with five-fold cross-validation to ensure robustness.ResultsThe training set, identified key variables associated with stroke prognosis, including hypertension, diabetes, and smoking history. …”
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Applications of Artificial Intelligence in Psychiatry and Psychology Education: Scoping Review by Julien Prégent, Van-Han-Alex Chung, Inès El Adib, Marie Désilets, Alexandre Hudon

“…Barriers included limited AI training, ethical concerns, lack of digital literacy, algorithmic opacity, and insufficient curricular integration. …”
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Exploring Transfer Learning for Anthropogenic Geomorphic Feature Extraction from Land Surface Parameters Using UNet by Aaron E. Maxwell, Sarah Farhadpour, Muhammad Ali

“…However, the operationalization of these supervised classification methods is limited by a lack of large volumes of quality training data. This study explores the use of transfer learning, where information learned from another, and often much larger, dataset is used to potentially reduce the need for a large, problem-specific training dataset. …”
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Les machines y voient-elles quelque chose ? by Denis Bonnay

“…Those representations present the specific traits associated with successful training: They are hierarchical, robust, and versatile. …”
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Dynamic emotion intensity estimation from physiological signals facilitating interpretation via appraisal theory. by Isabel Barradas, Reinhard Tschiesner, Angelika Peer

“…Using data-extracted physiological features, we train intrasubject and intersubject intensity models using a genetic algorithm, which outperform traditional sliding-window linear regression, providing a robust basis for interpretation. …”
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The poetry of senses: exploring semantic mediation in timbre-aroma correspondences by Asterios Zacharakis

“…Furthermore, distances between stimuli derived from semantic ratings and optimized through a genetic algorithm exhibited a strong correlation with previously estimated ground-truth distances of direct cross-modal associations. …”
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Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma by Haoyi Li, Mingming Xiong, Ming Li, Caixia Sun, Dao Zheng, Leilei Yuan, Qian Chen, Song Lin, Zhenyu Liu, Xiaohui Ren

“…The radiomic signature, consisting of 8 selected features, demonstrated strong predictive performance, with area under the curves of 0.994 in training cohort and 0.913 in validation cohort. This signature was independently associated with the DR in both cohorts. …”
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Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep by Fernando Amarilho-Silveira, Ignacio De Barbieri, Elly A. Navajas, Jaime Araujo Cobuci, Gabriel Ciappesoni

“…The dataset was randomly partitioned into two subsets: one for training (80%) the algorithms and the other for direct validation (20%). …”
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Predicting p53 Status in IDH‐Mutant Gliomas Using MRI‐Based Radiomic Model by Jiamin Li, Zhihong Lan, Xiao Zhang, Xiaoyun Liang, Hanwei Chen, Xiangrong Yu

“…The textural differences of peritumoral edema may more accurately reflect the underlying tumor heterogeneity associated with p53 status.…”
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Prediction of the volume of shallow landslides due to rainfall using data-driven models by J. Tuganishuri, C.-Y. Yune, G. Kim, S. W. Lee, M. D. Adhikari, S.-G. Yum

“…The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. …”
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Parallel Machine Production and Transportation Operations’ Scheduling with Tight Time Windows by Yang Jiang, Tong He, Jie Xiong, Xin Wu, Yao Chen

“…This study addresses a parallel machine production and transportation operations’ scheduling problem with a tight time window associated with the transfer and delivery process. The orders located at the parallel machines need to be delivered to customers by train. …”
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A real-time recursive dynamic model for vehicle driving simulators by M. Mirtaheri, M. Salkhordeh

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Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing by Waqar Shehbaz, Qingjin Peng

“…Subsequently, four ML models, Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, are trained and evaluated. Hyperparameter tuning is conducted using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Box constraints (L-BFGS-B) algorithm, which demonstrates the superior computational efficiency compared to traditional approaches such as grid and random search. …”
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Assessment of the application of each multibeam echosounder data product for monitoring of Laminaria digitata in the UK by Jacob Berry, Cassandra Nanlal

“…An image-based processing methodology using a random forests algorithm was used to generate classification models, which were trained and tested using ground truth samples obtained through video imagery. …”
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Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model by Elhoucine Ouassam, Yassine Dabachine, Nabil Hmina, Belaid Bouikhalene

“…To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. …”
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Evidence for preferred propagating terrestrial heatwave pathways due to Rossby wave activity by Mingzhao Wang, Yu Huang, Christian L. E. Franzke, Naiming Yuan, Zuntao Fu, Niklas Boers

“…Here, we examine the spatial propagation of terrestrial heatwaves using a complex network algorithm, and find four preferred propagation pathways of terrestrial heatwaves in the northern hemisphere. …”
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A Nomogram for Predicting Recurrence in Stage I Non‐Small Cell Lung Cancer by Rongrong Bian, Feng Zhao, Bo Peng, Jin Zhang, Qixing Mao, Lin Wang, Qiang Chen

“…The recurrence‐associated signature was developed by penalized Cox regression. …”
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Identification of novel markers for neuroblastoma immunoclustering using machine learning by Longguo Zhang, Longguo Zhang, Huixin Li, Huixin Li, Fangyan Sun, Fangyan Sun, Qiuping Wu, Qiuping Wu, Leigang Jin, Leigang Jin, Aimin Xu, Aimin Xu, Jiarui Chen, Ranyao Yang, Ranyao Yang, Ranyao Yang

“…Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.ResultsIn the training set, there were 362 samples in the immunity_L group and 136 samples in the immunity_H group, with differences in age, MYCN status, etc. …”
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