Diagnostic Analysis of Crosswalk Safety Hazards in Pedestrian Environments: A SHAP-Enhanced Machine Learning Approach With Street-View Imagery by Caryl Anne M. Barquilla, Jeongwoo Lee

“…It used data from 36,750 crosswalks in Seoul, South Korea, to rigorously evaluate multiple machine learning algorithms for predicting pedestrian crash risk. Among the models assessed, the Random forest (RF) demonstrated the highest precision, aligning with the objective of enhancing pedestrian safety through accurate risk identification. …”
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Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning by Nehir Uyar, Azize Uyar

“…The results revealed a strong correlation between agricultural expansion and increased C and N₂O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. …”
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Machine learning and deep learning in medicine and neuroimaging by Iván Sánchez Fernández, Jurriaan M. Peters

“…Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without being explicitly programmed. Deep learning algorithms learn by processing the data with increasing levels of abstraction in each layer. …”
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Identification of Fake Comments in E-Commerce Based on Triplet Convolutional Twin Network and CatBoost Model by Juanjuan Peng

“…At the same time, the accuracy of the four features was 0.8931, 0.9012, 0.8846, and 0.8961, respectively, which verified the excellent predictive recognition performance. Compared with the other two comparison algorithms, the detection time required by the proposed model was the shortest at 115us and consumed the least amount of resources. …”
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Artificial Intelligence in the Organization of Nursing Care: A Scoping Review by João Ventura-Silva, Maria Manuela Martins, Letícia de Lima Trindade, Ana da Conceição Alves Faria, Soraia Pereira, Samuel Spiegelberg Zuge, Olga Maria Pimenta Lopes Ribeiro

“…Conclusions: AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.…”
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Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. by Maggy T Sikulu-Lord, Michael D Edstein, Brendon Goh, Anton R Lord, Jye A Travis, Floyd E Dowell, Geoffrey W Birrell, Marina Chavchich

“…The spectra were analysed using machine learning to develop predictive models for infection.<h4>Findings</h4>Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). …”
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A cotton organ segmentation method with phenotypic measurements from a point cloud using a transformer by Fu-Yong Liu, Hui Geng, Lin-Yuan Shang, Chun-Jing Si, Shi-Quan Shen

“…This study proposes a cotton point cloud organ semantic segmentation method named TPointNetPlus, which combines PointNet++ and Transformer algorithms. Firstly, a dedicated point cloud dataset for cotton plants is constructed using multi-view images. …”
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Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques by Mustafa Muthanna Najm Shahrabani, Rasa Apanaviciene

“…The existing literature on assessment methodologies reveals diverging evaluation frameworks for smart buildings and smart cities, non-uniform metrics and taxonomies that hinder scalability, and the low use of machine learning in predictive integration modelling. To fill these gaps, this paper introduces a novel machine learning model to predict smart building integration into smart city levels and assess their impact on smart city performance by leveraging data from 147 smart buildings in 13 regions. …”
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Machine learning applied to the design and optimization of polymeric materials: A review by Sudarsan M. Pai, Karim A. Shah, Sruthi Sunder, Rodrigo Q. Albuquerque, Christian Brütting, Holger Ruckdäschel

“…Machine learning (ML) offers a promising solution to this issue. By leveraging ML algorithms, researchers can accelerate the design process, predict material properties more rapidly, and optimize formulations. …”
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Dynamic Processes of Development Jointing of a Fractal Type: Models for a Solid-State Material of the Chamber in a Power Facility during its Operation by T. V. Ryzhova, D. N. Bukharov, M. M. Arakelyan, S. M. Arakelyan

“…The availability of a reliable database of their characteristics and operating modes of the working substance in real conditions with numerical parameters should allow, within the framework of the considered concepts, to fulfil predictive modeling and prediction of the durability of safe and stable operation of such devices and control their modes, taking into account appropriate metrological support.…”
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Communicating the use of artificial intelligence in agricultural and environmental research by Aaron Lee M. Daigh, Samira H. Daroub, Peter M. Kyveryga, Mark E. Sorrells, Nithya Rajan, James A. Ippolito, Endy Kailer, Christine S. Booth, Umesh Acharya, Deepak Ghimire, Saurav Das, Bijesh Maharjan, Yufeng Ge

“…Clear communication is perhaps more necessary with AI than previous technologies due to its broad and flexible spectrum of uses, the “black‐box” nature of deep‐learning algorithms, and ongoing debates regarding AI's predictive power versus knowledge of first‐principles mechanistic and process‐based theories and models. …”
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Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion by Zifeng Zhang, Ning Li, Yuhang Qian, Huilin Cheng

“…Results This study developed 30 predictive models using ten classifiers across two imaging sequences. …”
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Integrating Model‐Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation by Karthik Raman, Rukmini Kumar, Cynthia J. Musante, Subha Madhavan

“…Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug–target interactions from big data, enabling more accurate predictions and novel hypothesis generation. …”
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A Probabilistic Approach to Surrogate‐Assisted Multi‐Objective Optimization of Complex Groundwater Problems by Reygie Q. Macasieb, Jeremy T. White, Damiano Pasetto, Adam J. Siade

“…We demonstrate the capabilities of the algorithm through benchmark test functions and a typical density‐dependent coastal groundwater management problem.…”
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Exploring alumina nanoparticle deposition in heat exchangers with hexagonal tubes: A hybrid approach integrating numerical simulations and machine learning by Seyed Hamed Godasiaei, Pouyan Talebizadehsardari, Amir Keshmiri

“…The proposed hybrid model demonstrated an impressive predictive accuracy of 97 % using the DNN algorithm, confirming its reliability and robustness for industrial applications.…”
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Artificial Intelligence in Digital Marketing: Towards an Analytical Framework for Revealing and Mitigating Bias by Catherine Reed, Martin Wynn, Robin Bown

“…Traditional AI centres on supervised learning algorithms to support and enable the application of data rules, predictive functionality and other AI-based features. …”
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FBiLSTM-Attention short-term load forecasting based on fuzzy logic by Yan ZHANG, Zepeng KANG, Xiaozhi GAO, Nan YANG, Zhaolei WANG

“…Aiming at the problem of high uncertainty in power load data due to various factors, a fuzzy logic based FBiLSTM Attention short-term load forecasting model was proposed by combining the uncertainty of load data with deep learning algorithms to improve the accuracy of load forecasting. …”
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Comparison of Recognition Techniques to Classify Wear Particle Texture by Mohammad Laghari, Ahmed Hassan, Mahmoud Haggag, Addy Wahyudie, Motaz Tayfor, Abdallah Elsayed

“…This analysis plays a vital role in predictive maintenance by revealing component degradation in machinery. …”
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Development and calibration of roundabout safety performance functions using machine learning: a case study from Amman, Jordan by Diana Al-Nabulsi, Aya Hassouneh

“…Comparative modeling was conducted using ordinary least squares regression and Random Forest Regressor algorithms. The linear regression model yielded an R 2 of 0.542 with a high sum of squared errors (SSE = 3750.38), underscoring its limited capacity to capture non-linear relationships. …”
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Optimizing Stroke Classification with Pre-Trained Deep Learning Models by Serra Aksoy, Pinar Demircioglu, Ismail Bogrekci

“…This study aims to investigate the use of deep learning techniques for predicting ischemic strokes with high accuracy, enabling earlier diagnosis and intervention. …”
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