Autocompleting inequality by Mike Zajko

“…This article analyzes the fine-tuning of generative AI as a process of social ordering, beginning with the encoding of cultural dispositions into LLMs, their containment and redirection into vectors of ‘safety’, and the subsequent challenge of these ‘guard rails’ by users. …”
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Human motor control during movements with high demands by Paul Kaufmann, Willi Koller, Elias Wallnöfer, Basilio Goncalves, Clara Scheer, Juliana Exel, Olivia Froschauer, Catherine Hlavac, Mathias Wiplinger, Lorenz Zweier, Arnold Baca, Hans Kainz

“…These are constituted of synergy vectors within the spinal cord that comprise the relative weightings of different co-active muscles. …”
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Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals by Bangbei Tang, Bangbei Tang, Mingxin Zhu, Mingxin Zhu, Zhian Hu, Yongfeng Ding, Shengnan Chen, Yan Li

“…Additionally, compared with the dataset without baseline processing, the model’s accuracy increases by 3.50%, and the F1-score increases by 6.33% on the dataset after baseline processing.ConclusionsThe combination of physiological signals and machine learning models can effectively classify drivers' olfactory preferences. …”
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Spatial Quantum Beating of Adjustable Biphoton Frequency Comb With High-Dimensional Frequency-Bin Entanglement by Rong Xue, Xin Yao, Xu Liu, Heqing Wang, Hao Li, Zhen Wang, Lixing You, Yidong Huang, Wei Zhang

“…In this paper, we realize an adjustable biphoton frequency comb and investigate its spatial quantum beating. Vector spontaneous four wave mixing processes in a piece of dispersion shifted fiber are produced by non-degenerate continuous wave pump lights with orthogonal polarizations to generate broadband biphoton states, and then a pulse shaper is used to realize the biphoton frequency comb with high-dimensional frequency-bin entanglement. …”
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Rapid classification of rice according to storage duration via near-infrared spectroscopy and machine learning by Chen Zhai, Wenxiu Wang, Man Gao, Xiaohui Feng, Shengjie Zhang, Chengjing Qian

“…Finally, three pattern recognition methods (K-nearest neighbor analysis, linear discriminant analysis, and least squares support vector machine (LS-SVM)) were compared for their effectiveness in constructing classification models. …”
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A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques by Suganya Athisayamani, Tamilazhagan S, A. Robert Singh, Jae-Yong Hwang, Gyanendra Prasad Joshi

“…These outputs are fused into a combined feature vector, which is then used by a meta-classifier for final predictions. …”
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Starting driving style recognition of electric city bus based on deep learning and CAN data by Dengfeng Zhao, Zhijun Fu, Chaohui Liu, Junjian Hou, Shesen Dong, Yudong Zhong

“…The sample data set of driving style is established by pre-processing the collected in-vehicle CAN bus data including the status of driving and vehicle motion, the data pre-processing method includes data cleaning, normalization and sample segmentation. …”
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Characterizing the pan-cancer role of exosomal miRNAs in metastasis across cancers by Piyush Agrawal, Gulden Olgun, Arashdeep Singh, Vishaka Gopalan, Sridhar Hannenhalli

“…Gene Ontology analysis revealed both general cancer processes, such as wound healing and epithelial cell proliferation, as well as cancer-specific processes, such as “angiogenesis” in the kidney and “ossification” in the lung. …”
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Development and use of a cognitive system model for solving problems of purposeful behavior by A. A. Solodov, V. M. Trembach

“…The knowledge base for the formation of adaptation plans of purposeful behavior of the intellectual system is developed.For the key procedures of the cognitive system functioning, the concepts of optimal estimates of state vectors and optimal control process are introduced, which allow to synthesize the best in a certain sense algorithms and technical devices. …”
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Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification by Santhakumar D, Gnanajeyaraman Rajaram, Elankavi R, Viswanath J, Govindharaj I, Raja J

“…Gene selection plays a crucial role in the pre-processing of microarray data, aiming to identify a small set of genes that enhances classification accuracy and reduces costs. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). …”
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Data-driven fault detection and positioning of eccentric rolls in roll-to-roll systems using wrap angle and sensor proximity by Yoonjae Lee, Minjae Kim, Jaehyun Noh, Gyoujin Cho, Changwoo Lee

“…The study employs the FCM method to enhance defect detection accuracy, using Support Vector Machine (SVM) as the classifier to consistently evaluate the effectiveness of selected feature sets in identifying and positioning eccentricity in R2R systems. …”
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Dynamics of Magnetization in Multilayer TbCo / FeCo Structures under the Influence of Femtosecond Optical Excitation by N. A. Ilyin, A. A. Klimov, N. Tiercelin, P. Pernod, E. D. Mishina, M. S. Gaponov, K. A. Brekhov, A. S. Sigov, V. L. Preobrazhensky

“…The need to study ultrafast processes in magnetism is due to the prospects for creating ultrafast magnetic recording and ultrafast spintronic devices. …”
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FloodGNN-GRU: a spatio-temporal graph neural network for flood prediction by Arnold Kazadi, James Doss-Gollin, Antonia Sebastian, Arlei Silva

“…Compared to existing approaches, FloodGNN-GRU (i) employs a graph-based model (GNN); (ii) operates on both spatial and temporal dimensions; and (iii) processes the water flow velocities as vector features, instead of scalar features. …”
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Analysis of Wind Regime and Sand Transport Potential in the Marginal Ergs of the Namak Lake, Central Iran by Ariyan Allahveisi, Mehran Maghsoudi, Abolfazl Qojezadeh Helani, Mehdi Safari namivandi

“…These results confirm the crucial role of climatic factors, topography, and sediment sources in the dynamism of aeolian landforms and geomorphic processes in the region.Innovation: This study applies a vector-based, quantitative approach to sand drift analysis using wind indices, helping explain dune formation patterns and predict future morphological changes. …”
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Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization by Waqar Ashiq, Samra Kanwal, Adnan Rafique, Muhammad Waqas, Tahir Khurshaid, Elizabeth Caro Montero, Alicia Bustamante Alonso, Imran Ashraf

“…Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. …”
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Highly efficient stacking ensemble learning model for automated keratoconus screening by Zahra J. Muhsin, Rami Qahwaji, Ibrahim Ghafir, Mo’ath AlShawabkeh, Muawyah Al Bdour, Saif Aldeen AlRyalat, Majid Al-Taee

“…A novel stacking ensemble model is developed using the selected features to improve corneal classification into NKC, SCKC, and CKC by integrating top tree-based classifiers (random forest, gradient boosting, decision trees) with a support vector machine meta-classifier. Results The pre-processing and feature selection techniques reduced the model's parameters to just 6.33% of the original dataset, improving classification performance, and cutting over 85% of the training time. …”
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Fine-Grained Feature Extraction in Key Sentence Selection for Explainable Sentiment Classification Using BERT and CNN by Thennakoon Mudiyanselage Anupama Udayangani Gunathilaka, Jinglan Zhang, Yuefeng Li

“…These models rely on sequential processing of text rather than parallel processing, which can lead to missed important fine-grained features and long-range dependencies spread across sentences. …”
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Cryptocurrency forensics automation: a deep learning and NLP-based approach for mobile platforms by Abhishek Bhattarai, Abdulhadi Sahin, Maryna Veksler, Ahmet Kurt, Devrim Aras, Carlos Imery, Kemal Akkaya

“…Therefore, in this paper, we present a comprehensive framework that incorporates various machine learning (ML), image processing, and natural language processing (NLP) approaches to enable fast and automated extraction/triage of crypto-related artifacts from Android and iOS devices. …”
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Quantum-enhanced beetle swarm optimized ELM for high-dimensional smart grid intrusion detection by Na Cheng, Shuqing Wang, Lihong Zhao, Yan Hu

“…With regard to the efficiency of training, QBOA-ELM demonstrates a substantial advantage, processing large-scale datasets in a training time of 120 s, which is considerably less than the 180 s required by BOA-ELM and the 300 s required by SVM. …”
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