An Automated Framework for Lane Closure Detection on Highway Using Connected Vehicle Data and Machine Learning Models by Ashutosh Dumka, Raghupathi Kandiboina, Aparna Joshi, Skylar Knickerbocker, Neal Hawkins, Anuj Sharma

“…We employ two machine learning models—support vector machines (SVMs) and k-nearest neighbors (K-NN)—trained on features extracted from these metrics to detect lane closures and provide insights into location and time of start and end. …”
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NLP for computational insights into nutritional impacts on colorectal cancer care by Shengnan Gong, Xiaohong Jin, Yujie Guo, Jie Yu

“…ATSO-LLMs are employed to analyze the processed dietary data, identifying key nutritional factors and forecasting CRC and non-CRC phenotypes based on dietary patterns. The results show that combining NLP-derived features with ATSO-LLMs significantly enhances prediction accuracy (98.4 %), sensitivity (97.6 %) specificity (96.9 %) and F1-Score (96.2 %), with minimal misclassification rates. …”
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In silico identification of potential calcium dynamics and sarcomere targets for recovering left ventricular function in rat heart failure with preserved ejection fraction. by Stefano Longobardi, Anna Sher, Steven A Niederer

“…The model simulated left ventricular (LV) pressure-volume loops that were described by 14 scalar features. We trained a Gaussian process emulator to map the 16 input parameters to each of the 14 outputs. …”
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Soil organic carbon retrieval using a machine learning approach from satellite and environmental covariates in the Lower Brazos River Watershed, Texas, USA by Birhan Getachew Tikuye, Ram Lakhan Ray

“…All explanatory variables are geospatial gridded datasets, except for the point-based measurement of SOC on the Prairie View A&M University (PVAMU) research farm plot used to train the model. …”
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Influence of the parameters of the car wheel flanges on the safety operation in turnouts by B. E. Glyuzberg

“…The experience of several decades of operation is confirmed by the fact that this regulatory framework provides for the transportation process and the safety of the movement of trains in turnouts. Introduction of changes in the “wheelset - rail gauge” measuring system should be based on analysis of the actual state of wheel sets and the parameters of the track gauge in turnouts. …”
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System Approach to the Combined Use of Large Language Models and Classical Models in Foresight Tasks by Володимир Савастьянов, Михайло Столяр

“…The article highlights features offered by GPT-based models and compares the conclusions with classical methods of textual data analysis in classification tasks using the prediction methodology as an example. …”
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Different pixel sizes of topographic data for prediction of soil salinity. by Shima Esmailpour, Ebrahim Mahmoudabadi, Mohammad Ghasemzadeh Ganjehie, Alireza Karimi

“…The model's effectiveness was evaluated based on their Root Mean Square Error (RMSE) and coefficient of determination (R2). …”
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Data Analytics and Machine Learning Models on COVID-19 Medical Reports Enhanced with XAI for Usability by Oliver Lohaj, Ján Paralič, Zuzana Paraličová, Daniela Javorská, Elena Zagorová

“…The best model for predicting mortality risk was also the LightGBM model, which achieved a ROC AUC score of 83.7% and a classification accuracy of 81.2% using all features. Using a simplified model trained on the 15 most important features, the ROC AUC score was 83.6% and the classification accuracy was 80.5%. …”
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A Super-Resolution Approach for Image Resizing of Infant Fingerprints With Vision Transformers by Joao H. P. Machado, Barbara De O. Koop, Marcelo Filipak, Marco A. C. Barbosa, Jefferson T. Oliva, Luiz Fernando Puttow Southier, Dalcimar Casanova, Marcelo Teixeira

“…This paper proposes an approach for image resizing based on Super-Resolution networks that train Vision Transformer (ViT) how to handle newborn fingerprints, avoiding the introduction of noises while keeping and improving important details for biometric recognition. …”
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A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection by Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng

“…Although several machine learning–based detection models have been proposed, there is still a need for improved accuracy. …”
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GreenNet: A dual-encoder network for urban green space classification using high-resolution remotely sensed images by Ke Chen, Yang Wang, Cunrui Huang, Jing Wang, Sabrina L. Li, Haiyan Guan, Lingfei Ma

“…Additionally, a transformer-based outside-global–local attention block (OGLAB) is developed to fuse the intra-image and inter-image features from the dual-encoder to effectively capture inherent semantic representations of urban green spaces. …”
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Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion... by Wael I. Ibraheem, Saurabh Jain, Mohammed Naji Ayoub, Mohammed Ahmed Namazi, Amjad Ismail Alfaqih, Aparna Aggarwal, Abdullah A. Meshni, Ammar Almarghlani, Abdulkareem Abdullah Alhumaidan

“…This data served as reference data. A pre-trained AI-based computer-aided detection (“CADe”) software (Second Opinion^®, version 1.1) was used for the detection of the predefined features. …”
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A Multi-Stream Approach to Mixed-Traffic Accident Recognition Using Deep Learning by Swee Tee Fu, Lau Bee Theng, Brian Loh Chung Shiong, Chris McCarthy, Mark Tee Kit Tsun

“…Road traffic accidents are a leading cause of injuries and fatalities globally, prompting extensive research into deep learning-based accident recognition models for their superior performance in computer vision tasks. …”
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A case study in statistical software development for advanced evidence synthesis: the combined value of analysts and research software engineers by Naomi Bradbury, Tom Morris, Clareece Nevill, Janion Nevill, Ryan Field, Suzanne Freeman, Nicola Cooper, Alex Sutton

“…We aim to increase our user base by implementing new features within the apps and building links with other groups developing complementary evidence synthesis tools.…”
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Handwritten Urdu Characters and Digits Recognition Using Transfer Learning and Augmentation With AlexNet by Aqsa Rasheed, Nouman Ali, Bushra Zafar, Amsa Shabbir, Muhammad Sajid, Muhammad Tariq Mahmood

“…The purpose of this research is to present a classification framework for automatic recognition of handwritten Urdu character and digits with higher recognition accuracy by utilizing theory of transfer learning and pre-trained Convolution Neural Networks (CNN). The performance of transfer learning is evaluated in different ways: by using pre-trained AlexNet CNN model with Support Vector Machine (SVM) classifier, and fine-tuned AlexNet for extracting features and classification. …”
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Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application by Chao Li, Ji Chen, Mengshi Dong, Hao Yan, Feng Chen, Ning Mao, Shuai Wang, Xiaozhu Liu, Yanqing Tang, Fei Wang, Jie Qin

“…Resting-state functional connectivity (FC) features were used to train a Support Vector Machine (SVM) classifier on individuals with medicated chronic SSD and HCs from three sites. …”
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Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning by Jakub Horvath, Pavel Jedlicka, Marie Kratka, Zdenek Kubat, Eduard Kejnovsky, Matej Lexa

“…Results We used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting), (ii) hybrid convolutional/long and short memory network models, and (iii) a DNA pre-trained transformer-based model using k-mer sequence representation. …”
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Ways to improve the efficiency of automatic control systems of pressing current collectors for railway service of high speeds by A. S. GOLUBKOV, O. A. SIDOROV, S. N. SMERDIN

“…One of the limiting factors in increasing train speeds on railway transport is the quality of current collection. …”
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Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach by Shuai Liu, Peng Zhang, Anbin Wang, Keke Tang, Shuo Chen, Chensen Lin

“…Deterministic Lateral Displacement (DLD) has been widely utilized for the high-throughput and efficient separation of microspheres, cells, exosomes, and proteins, playing a crucial role in size-based particle separation. The high performance of DLD devices in various tasks relies on optimal design. …”
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ML-Driven Transistor Self-Heating Analysis From TCAD to Large IP Circuits by Simon Thomann, Nico Mayr, Albi Mema, Hussam Amrouch

“…We explore our method to study self-heating in complex large circuits featuring over 3700 transistors. This includes foundries IPs like a complete high-performance SRAM-based register file. …”
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