Handwriting Difficulties: A Review of Recent Advances in the Identification and Intervention by Suatmi Murnani, Kazuharu Hashitsume, Tomohiko Igasaki

“…Key techniques examined include the use of spatiotemporal signals, image data, feature-extraction methods, and algorithmic classification. We also explored key experimental design elements and assistive interventions aimed at improving handwriting performance. …”
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Machine learning discovery of the dielectric properties of strontium-containing condensed matter by Dongyang Huang, Jiaxing Fu, Chenghao Yu

“…This model serves as a valuable reference and guide, improving the efficiency of dielectric material selection and the discovery of novel high-performance dielectrics.…”
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Deriving and applying core competencies for intellectual property-based startup entrepreneurs: a data-driven approach to technology evaluation by Wongyu Lee, Jaeho Shin, Donghee Yoo

“…Abstract This study identifies the core competencies of managers of intellectual property (IP)-based startups and proposes an evaluation model to enhance managerial competency criteria during technology assessments. …”
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Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

“…The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. …”
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Optimized Quality of Service for Real-Time Wireless Sensor Networks Using a Partitioning Multipath Routing Approach by Mohammed Zaki Hasan, Tat-Chee Wan

“…The proposed approach significantly improves the network lifetime, while reducing energy consumption and decreasing average end-to-end delays within the sensor network via optimised resource sharing in intermediate nodes compared with existing routing algorithms.…”
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Impact of Subjective and Objective Green Space Characteristics on Mental Health Benefits: An Explainable Machine Learning Approach by Ke LI, Yipei MAO, Yongjun LI

“…Based on the SHAP values, the non-linear relationships between them are further clarified.ResultsThrough the analysis of 3 types of mental health benefits and 5 models, the LightGBM model outperforms other algorithms (such as Random Forest and XGBoost) in terms of prediction accuracy (R 2: 0.523 – 0.642), with its robustness in capturing complex feature interactions being verified. …”
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AI-powered approaches for enhancing remote sensing-based water contamination detection in ecological systems by Li Yang, Zhang Ziwen, Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Recent advancements in artificial intelligence (AI) offer promising solutions to enhance water contamination detection, particularly by leveraging machine learning algorithms and sensor networks for continuous monitoring.MethodsThis paper presents a novel AI-powered approach for improving water contamination detection, which incorporates real-time data processing and predictive modeling to identify contamination events and optimize response strategies. …”
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iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

“…By reducing the technical burden of coding, iMESc allows researchers to focus on scientific inquiry, improving both the efficiency and depth of their analyses.…”
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Multi-crop plant counting and geolocation using a YOLO-Powered GUI System by Renato Herrig Furlanetto, Nathan Schawn Boyd, Ana Claudia Buzanini

“…Crop counting has traditionally relied on manual field assessments or complex machine learning algorithms, which often struggle to identify small objects or underestimate the total count of objects present in the field. …”
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Marine Voyage Optimization and Weather Routing with Deep Reinforcement Learning by Charilaos Latinopoulos, Efstathios Zavvos, Dimitrios Kaklis, Veerle Leemen, Aristides Halatsis

“…Both DRL models demonstrate high adaptability to dynamic weather updates, achieving up to 12% improvement in fuel consumption compared to the distance-based baseline model. …”
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Deep learning (DL)‐based advancements in prostate cancer imaging: Artificial intelligence (AI)‐based segmentation of 68Ga‐PSMSA PET for tumor volume assessment by Sharjeel Usmani, Khulood Al Riyami, Subash Kheruka, Shah P Numani, Rashid al Sukaiti, Maria Ahmed, Nadeem Pervez

“…Artificial intelligence (AI)‐based segmentation techniques offer a promising solution to these challenges. AI algorithms, such as deep learning‐based models, have shown remarkable performance in automating tumor segmentation tasks with high accuracy and efficiency. …”
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Closed-Loop Clustering-Based Global Bandwidth Prediction in Real-Time Video Streaming by Sepideh Afshar, Reza Razavi, Mohammad Moshirpour

“…To overcome these challenges, we propose closed-loop clustering-based Global Forecasting Models (GFMs) for SBP. Unlike local models, GFMs apply the same function to all traces enabling cross-learning, and leveraging relationships among traces to address the performance issues seen in current SBP algorithms. …”
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Artificial Intelligence (AI) and Drug-Induced and Idiosyncratic Cytopenia: The Role of AI in Prevention, Prediction, and Patient Participation by Emmanuel Andrès, Amir El Hassani Hajjam, Frédéric Maloisel, Maria Belén Alonso-Ortiz, Manuel Méndez-Bailón, Thierry Lavigne, Xavier Jannot, Noel Lorenzo-Villalba

“…With its ability to process vast amounts of clinical, genomic, and pharmacovigilance data, AI can identify patterns and risk factors that may be missed by traditional methods. Machine learning algorithms can refine predictive models, enabling earlier detection and more accurate risk assessments. …”
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Development and validation of a nomogram for predicting in-hospital mortality in older adult hip fracture patients with atrial fibrillation: a retrospective study by Zhenli Li, Jing He, Tiezhu Yao, Guang Liu, Jing Liu, Ling Guo, Mengjia Li, Mengjia Li, Zhengkun Guan, Zhengkun Guan, Ruolian Gao, Jingtao Ma

“…Logistic regression (LR) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were employed to screen features. We further used Extreme Gradient Boosting (XGBoost) based on features selected by LR and LASSO algorithms to assist in identifying the final model-established features. …”
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Wave propagation-based tests for concrete piles – an overview by Reham Samaan, Abdelsalaam Mokhtar, Mohamed Saafan, Ahmed Ebid

“…Advanced computer techniques now enable more precise test analysis and structural assessments, significantly improving foundation evaluation and safety. This investigation establishes the practical and theoretical basis for implementing advanced machine learning predictive models that combine previous records with pattern recognition algorithms, potentially converting traditional PIT interpretation from an uncertain process to a reliable and precise evaluation system. …”
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Single Vector Hydrophone DOA Estimation: Leveraging Deep Learning with CNN-CBAM by Fanyu ZENG, Yaning HAN, Hongyuan YANG, Dapeng YANG, Fan ZHENG

“…Specifically, CBAM improves the model’s focus on subtle directional cues in noisy environments, suppressing irrelevant interference while amplifying essential signal components, which is crucial for an accurate DOA estimation. …”
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Regional Sea Surface Skin Temperature Retrieval From HY-1C and HY-1D COCTS by Zhuomin Li, Rui Chen, Mingkun Liu, Lei Guan

“…These satellites carry Chinese ocean color and temperature scanners (COCTS), containing two thermal bands with central wavelengths of 10.8 and 12.0 μm and enabling sea surface temperature (SST) observations. Herein, regional algorithms for SST retrieval are developed for the South China Sea (SCS) by applying radiative transfer modeling. …”
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Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions by Giuseppe Miceli, Maria Grazia Basso, Elena Cocciola, Antonino Tuttolomondo

“…Developing robust, generalizable AI models and integrating multimodal imaging data promise to enhance diagnostic and prognostic capabilities in TCD and ultrasound. …”
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A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings by Inho Jo, Yunku Lee, Namhyuk Ham, Juhyung Kim, Jae-Jun Kim

“…Additionally, it provides important empirical data, a validated methodological framework, and specific quantitative guidelines for standardizing UAV data collection workflows, thereby improving existing empirical or case-specific approaches.…”
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What is the impact of longer patient travel distances and times on perioperative outcomes following revision knee replacement: a retrospective observational study using data for En... by Sarah Lamb, Tim Briggs, Andrew James Price, William Gray, Andrew D Toms, Jonathan Peter Evans, Jonathan Thomas Evans, Alexander Handel Matthews

“…Evolving practice in England aims to manage these patients in specialised centres with the intention of improving outcomes. This practice will result in longer travel distances and times in this frailer group of patients. …”
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