Detection and Recognition Algorithm for 100-Meter Signage in Urban Rail Transit Based on Connected Domain Segmentation and SVM by YUAN Xiaojun, TIAN Ye, SU Zhen, LIU Xinwu, LI Chen, ZHANG Huiyuan

“…This general algorithm was then modified and optimized, leading to the development of a 100-meter signage detection and recognition algorithm that utilizes connected domain segmentation for detection and a support vector machine (SVM) for digit recognition. …”
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A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx by Akella S Narasimha Raju, K Venkatesh, Makineedi Rajababu, Ranjith Kumar Gatla, Marwa M. Eid, Enas Ali, Nataliia Titova, Ahmed B. Abou Sharaf

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Fast and Accurate Prediction of Electromagnetic and Temperature Fields for SPMSM Equipped with Unequally Thick Magnetic Poles by Feng Liu, Xiuhe Wang, Lingling Sun, Hongye Wei

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Impact of Urban Expansion on School Quality in Compulsory Education: A Spatio-Temporal Study of Dalian, China by Zhenchao Zhang, Weixin Luan, Chuang Tian, Min Su

“…With rapid urbanization, improving school quality in compulsory education is critical for optimal educational resource allocation. This study integrates a random forest machine learning model, GIS spatial analysis, and a spatial econometric model to examine the spatiotemporal differentiation of school quality in Dalian, China, in 2016 and 2020, as well as its relationships with the construction land development cycle, population density, and housing prices. …”
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Integrated impact of urban mixed land use on TOD ridership: A multi-radius comparative analysis by Xinyue Gu, Siyan Lin, Chengfang Wang

“…Subsequently, a Light Gradient Boosting Machine (LightGBM) classification model, complemented by SHapley Additive exPlanations (SHAP) values for interpretation, quantitatively evaluates the influence of mixed land use on TOD ridership across various catchment areas. …”
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Comprehensive Analysis on Complementary FET by Ayush Bhardwaj, Debashish Dash, Aditi Anant, Ved M. Bhanushali, Adarsh Kushwah, Ipshita Mishra, Shubham, Chandan Kumar Pandey

“…Emerging technologies such as monolithic 3D integration and machine learning predictions for design optimization are also discussed. …”
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AI-driven innovation in antibody-drug conjugate design by Heather A. Noriega, Xiang Simon Wang

“…Computational methods, including artificial intelligence and machine learning (AI/ML) are increasingly being integrated into ADC discovery and optimization workflows (i.e., AI-driven ADC Design) to address these limitations. …”
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Advancing Smart Energy: A Review for Algorithms Enhancing Power Grid Reliability and Efficiency Through Advanced Quality of Energy Services by José M. Liceaga-Ortiz-De-La-Peña, Jorge A. Ruiz-Vanoye, Juan M. Xicoténcatl-Pérez, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, Ricardo A. Barrera-Cámara, Daniel Robles-Camarillo, Marco A. Márquez-Vera, Francisco R. Trejo-Macotela, Luis A. Ortiz-Suárez

“…We employ multiple machine learning models—including Random Forest, XGBoost version 1.6.1 and Long Short-Term Memory (LSTM) networks—to predict future energy demand. …”
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Hybrid AI-Based Framework for Renewable Energy Forecasting: One-Stage Decomposition and Sample Entropy Reconstruction with Least-Squares Regression by Nahed Zemouri, Hatem Mezaache, Zakaria Zemali, Fabio La Foresta, Mario Versaci, Giovanni Angiulli

“…To optimize forecasting accuracy, outputs from all models are combined using a least-squares regression technique that assigns optimal weights to each prediction. …”
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FedEmerge: An Entropy-Guided Federated Learning Method for Sensor Networks and Edge Intelligence by Koffka Khan

“…<b>Introduction:</b> Federated Learning (FL) is a distributed machine learning paradigm where a global model is collaboratively trained across multiple decentralized clients without exchanging raw data. …”
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BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification by Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, André C.P.L.F. de Carvalho

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Magnetoelectric nanoparticle technology for mitigating motor deficits in Parkinsonian mice by David Dominguez-Paredes, Yasin Temel, Aleksandra Milojkovic, Daniëlle Mulder-Jongen, Prachi Kumari, Mario Ries, Kristen Kozielski, Ali Jahanshahi

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Relationship between stress hyperglycemia ratio and progression of non target coronary lesions: a retrospective cohort study by Shiqi Liu, Ziyang Wu, Gaoliang Yan, Yong Qiao, Yuhan Qin, Dong Wang, Chengchun Tang

“…Patients were classified into progression and non progression groups based on follow-up angiography findings. Logistic regression models, restricted cubic spline analysis, and machine learning algorithms (LightGBM, decision tree, and XGBoost) were utilized to analyse the relationship of stress hyperglycemia ratio and non target lesion progression. …”
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Inertial-Based Gait Metrics During Turning Improve the Detection of Early-Stage Parkinson&#x2019;s Disease Patients by Lin Meng, Jun Pang, Yifan Yang, Lei Chen, Rui Xu, Dong Ming

“…Sensitive gait features were optimally screened (AUC<inline-formula> <tex-math notation="LaTeX">$&gt;$ </tex-math></inline-formula>0.7) and categorized into 22 groups to classify PD and healthy controls based on a machine learning method. …”
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Torque capability enhancement of commercial interior permanent magnet motors using t-shaped notching and merged barrier rotor topology by H.N. Lin, P. Seangwong, N. Fernando, A. Siritaratiwat, P. Khunkitti

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G-TS-HRNN: Gaussian Takagi–Sugeno Hopfield Recurrent Neural Network by Omar Bahou, Mohammed Roudani, Karim El Moutaouakil

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Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm by Mohammad Reza Komari Alaei, Reza Rostamzadeh, Kadir Albayrak, Zenonas Turskis, Jonas Šaparauskas

“… Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. …”
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Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals by Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

“…Privacy concerns and regulatory restrictions further limit data sharing, making conventional centralized machine learning (ML) approaches less effective. To address these challenges, this study proposes a federated learning (FL)-based solution that enables multiple healthcare organizations to collaboratively train a global model without sharing raw patient data, thereby enhancing model performance while ensuring data privacy and security. …”
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Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts by Thompson Stephan, Padma Priya Dharishini Paramana, Chia-Chen Lin, Saurabh Agarwal, Rajan Verma

“…A Peltier cooling module and a humidifier maintain optimal conditions. Machine learning models classify food freshness, while federated learning ensures vendor privacy by training models locally on each cart. …”
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An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing by Vivek Kumar Pandey, Shiv Prakash, Aditya Ranjan, Sudhanshu Kumar Jha, Xin Liu, Rajkumar Singh Rathore

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