Zooming into Berlin: tracking street-scale CO2 emissions based on high-resolution traffic modeling using machine learning by Max Anjos, Fred Meier

“…Using data-driven algorithms, traffic counts, spatio-temporal features, and meteorological data, our model predicted hourly traffic flow, average speed, and CO2 emissions for passenger cars (PC) and heavy-duty trucks (HDT) at the street scale in Berlin. …”
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Approximation-Aware Training for Efficient Neural Network Inference on MRAM Based CiM Architecture by Hemkant Nehete, Sandeep Soni, Tharun Kumar Reddy Bollu, Balasubramanian Raman, Brajesh Kumar Kaushik

“…This architecture includes a mapping algorithm that modulates inputs and map AFC to crossbar arrays directly, eliminating the need to predict approximated weights for evaluating output. …”
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Ultra-Short-Term Forecasting-Based Optimization for Proactive Home Energy Management by Siqi Liu, Zhiyuan Xie, Zhengwei Hu, Kaisa Zhang, Weidong Gao, Xuewen Liu

“…This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. …”
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Optimizing Natural Image Quality Evaluators for Quality Measurement in CT Scan Denoising by Rudy Gunawan, Yvonne Tran, Jinchuan Zheng, Hung Nguyen, Rifai Chai

“…Evaluating the results of image denoising algorithms in Computed Tomography (CT) scans typically involves several key metrics to assess noise reduction while preserving essential details. …”
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Recurrent academic path recommendation model for engineering students using MBTI indicators and optimization enabled recurrent neural network by Anupama V, Sudheep Elayidom M

“…Abstract In recent years, the rapid expansion of online learning content has made it increasingly difficult for students to identify suitable educational pathways. This challenge is particularly significant for engineering students, who often require structured guidance to select appropriate academic courses. …”
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Urban pulse: measuring pace of life through public camera-based cadence analysis by Pei-Yu Ho, Ming-Kuang Chung, Ling-Jyh Chen

“…This approach has been applied using various live public cameras to evaluate pedestrian cadence, examining differences between diverse geographical and functional zones (residential, educational, touristic, business, and traffic areas). …”
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Ecological Identity and Social Representation of Green MSMEs: Ergo-Iconic Value, Performativity, and Representational Resistance in the Platform Economy by Andriyansah Andriyansah

“…The findings reveal three dominant patterns: (1) the symbolization of ergo-iconic value, combining tangible ecological practices with aesthetic visualizations as symbolic capital; (2) performative and algorithm-driven sustainability, where content is crafted to attract visibility within a competitive platform ecosystem; and (3) representational resistance, where certain MSMEs choose to highlight complex ecological processes that may not be “Instagrammable” but are instead educational and contextually grounded. …”
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Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment by Fabio Stagno, Giuseppe Mirabile, Patricia Rizzotti, Adele Bottaro, Antonio Pagana, Sebastiano Gangemi, Alessandro Allegra

“…Artificial intelligence (AI) can improve risk assessment and diagnosis, as well as boost the precision of clinical outcome prediction and illness classification. Algorithms based on artificial intelligence may be potentially helpful in discovering new needs for myelodysplastic syndrome-affected patients, choosing treatment and assessing minimal residual disease. …”
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Cooperative and Hierarchical Optimization Design of Shipboard MVDC System for Adapting to Large, Pulsed Power Load by Zhimeng Liu, Yongbao Liu, Youhong Yu, Rui Yang

“…The SVSDP scheme achieves reductions of 19.5%, 7.6%, and 49.6%, 39.7% compared to VRCD and PSO-FS. …”
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Modeling emotional abuse based on emotional maturity and self-differentiation with the mediating role of marital intimacy by mehran sharifi, qumarth karimi, omid moradi, shokooh navabinejad

“…Data analysis was analyzed using Pearson correlation method, structural equation modeling and partial least squares (SMARTPLS-3) algorithm and SPSS-23. Results: The results indicated the fit of the general research model with the experimental data. …”
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Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease by Xiachuan Qin, Xiaoling Liu, Linlin Xia, Qi Luo, Chaoxue Zhang

“…The AUC of the multimodal US DL model was significantly better than that of the single-mode DL and clinical models. The DL algorithm developed using multimodal US images can effectively predict early fibrosis in patients with CKD with significantly greater accuracy than single-mode DL or clinical models.…”
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Application of artificial neural networks in the drilling processes: Can equivalent circulation density be estimated prior to drilling? by Husam H. Alkinani, Abo Taleb T. Al-Hameedi, Shari Dunn-Norman, David Lian

“…Multiple training functions were tested to train the network, Bayesian regularization (BR) algorithm was chosen from the other algorithms since it had the lowest MSE and the highest R-squared. …”
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Classifying detrital zircon U-Pb age distributions using automated machine learning by Jack W. Fekete, Glenn R. Sharman, Xiao Huang

“…Applied to the North American Cordillera dataset, AutoML achieves an ∼0.91 F1 score when predicting between foreland and forearc basin tectonic settings and an ∼0.71 F1 score when predicting subbasins within these settings, outperforming both RF and R2. …”
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Chlorophyll-a in the Chesapeake Bay Estimated by Extra-Trees Machine Learning Modeling by Nikolay P. Nezlin, SeungHyun Son, Salem I. Salem, Michael E. Ondrusek

“…Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. …”
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Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments by Amin Taheri-Garavand, Mojgan Beiranvandi, Abdolreza Ahmadi, Nikolaos Nikoloudakis

“…The current research was carried out to develop a genetic algorithm-based artificial neural network (ΑΝΝ) model able of simulating the levels of antioxidants in savory when using soil amendments [biochar (BC) and superabsorbent (SA)] under drought. …”
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Integrating Street View Images, Deep Learning, and sDNA for Evaluating University Campus Outdoor Public Spaces: A Focus on Restorative Benefits and Accessibility by Tingjin Wu, Deqing Lin, Yi Chen, Jinxiu Wu

“…On this basis, restorative benefit evaluation models were established, including the explanatory and predictive models. The explanatory model used Pearson’s correlation and multiple linear regression analysis to identify the key indicators affecting restorative benefits, and the predictive model used the XGBoost 1.7.3 algorithm to predict the restorative benefit scores on the campus scale. …”
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The Prognostic Value of Serum HBV-RNA during Hepatitis B Virus Infection is Related to Acute-on-Chronic Liver Failure by Keli Qian, Ying Xue, Hang Sun, Ting Lu, Yixuan Wang, Xiaofeng Shi

“…A nomogram was developed to formulate an algorithm incorporating serum HBV-RNA for predicting the survival of HBV-ACLF patients. …”
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Comparative performance of deep learning architectures for diabetic peripheral neuropathy detection using corneal confocal microscopy: a retrospective single-centre study by Yuyang Deng, Wenqu Chen, Weihuang Xu, Jianzhang Hu

“…For single-image predictions in the three-class classification task of CCM images, the InceptionV3 model achieved a precision of 0.8385, a recall of 0.9083, an F1 score of 0.8720 and an AUC of 0.8769 for predicting DPN+.Conclusions The InceptionV3-based DLA model achieved superior performance compared with traditional convolutional neural network architectures like ResNet and DenseNet, and the Swin transformer model, highlighting its potential for effective DPN screening.…”
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Machine learning modeling of cancer treatment-related cardiac events in breast cancer: utilizing dosiomics and radiomics by Sefika Dincer, Muge Akmansu, Oya Akyol

“…Machine learning models were optimized using the Tree-based Pipeline Optimization Tool (TPOT), identifying the gradient-boosted classification as the best-performing algorithm. Feature selection was conducted using gradient-boosted recursive feature elimination. …”
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Prognostic Value of a Classification and Regression Tree Model in Patients with Open-Globe Injuries by Danica T. Esteban, MD, Karlo Marco D. Claudio, MD, Cheryl A. Arcinue, MD

“…Purposive sampling of hospital medical records was done to collect data from both in- and out-patient cases. The CART algorithm was utilized to determine the predicted visual outcome for each case, and the accuracy of prognostication was measured by computing for sensitivity, specificity, positive predictive value, and negative predictive value. …”
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