Development of Non-Invasive Continuous Blood Pressure Measurement System Based on Multiple Feature Parameters by Mingyue LI, Zhiying YUAN, Ruowei LI, Jieying SHAN, Jilun YE, Xu ZHANG, Hui YU, Xiaoyu WU

“…A PC-based human-computer interaction software platform was constructed, and continuous blood pressure measurement-related algorithms were integrated. Multiple feature parameters such as pulse wave transit time based on synchronous ECG and PPG signals were extracted, enabling non-invasive continuous blood pressure measurement. …”
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Optimising Grid-Connected PV-Battery Systems for Energy Arbitrage and Frequency Containment Reserve by Rodolfo Dufo-López, Juan M. Lujano-Rojas, Jesús S. Artal-Sevil, José L. Bernal-Agustín

“…By applying genetic algorithms (GA), the optimal configurations of PV generators, inverters/chargers, and batteries were determined, focusing on maximising the net present value (NPV). …”
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Correlation Between Clinical Indicators and Liver Pathology in Children with Chronic Hepatitis B by Chenyang Huang, Ying Lu, Ziwei Wang, Qiyu Jiang, Yi Dong, Lili Cao, Jianguo Yan, Zhiqiang Xu, Fuchuan Wang, Yinjie Gao, Junliang Fu, Min Zhang, Fu-Sheng Wang

“…Our findings highlight the value of integrating age and key biochemical markers into non-invasive diagnostic algorithms for the early detection and management of liver pathology in children.…”
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Beyond purchase patterns: harnessing predictive analytics to anticipate unarticulated consumer needs by Khaled Alshaketheep, Hind Al-Ahmed, Ahmad Mansour

“…The results underscore the importance of integrating human-centered design with algorithmic precision to translate predictive insights into actionable business value.…”
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Leveraging Feature Fusion of Image Features and Laser Reflectance for Automated Fish Freshness Classification by Caner Balım, Nevzat Olgun, Mücahit Çalışan

“…Fish is important for human health due to its high nutritional value. However, it is prone to spoilage due to its structural characteristics. …”
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Artificial Intelligence and the Future of Mental Health in a Digitally Transformed World by Aggeliki Kelly Fanarioti, Kostas Karpouzis

“…The paper argues for a value-driven approach that centers equity, transparency, and informed consent in the deployment of AI tools. …”
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Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms by Jorge Enrique Chaparro, José Edinson Aedo, Felipe Lumbreras Ruiz

“…These sources included multispectral images captured by an unmanned aerial vehicle (UAV); in situ sensors, which collected information on ecological factors such as pH, temperature, solar radiation, relative humidity, soil moisture, wind speed and direction, as well as SPAD values indicating leaf chlorophyll content.Total nitrogen (TN) values were taken from leaf tissue samples, which were then analyzed in a laboratory. …”
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Flow Improvement in Evacuation Planning with Budget Constrained Switching Costs by Ram Chandra Dhungana, Tanka Nath Dhamala

“…Moreover, we introduce the static and dynamic maximum flow problems with lane reversal strategy and also propose efficient algorithms for their solutions. Here, the contraflow approach reverses the direction of arcs with respect to the lane reversal costs to increase the flow value. …”
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Multi-Objective Optimization Methods for University Campus Planning and Design—A Case Study of Dalian University of Technology by Lin Qi, Chaoran Chen, Jun Dong

“…This study focuses on the multi-objective coordination problem in university campus planning and design, proposing an optimized methodology integrating an improved multi-objective decision-making framework. …”
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METHODS OF DISTANCE MEASUREMENT’S ACCURACY INCREASING BASED ON THE CORRELATION ANALYSIS OF STEREO IMAGES by V. L. Kozlov

“…To solve the problem of increasing the accuracy of restoring a three-dimensional picture of space using two-dimensional digital images, it is necessary to use new effective techniques and algorithms for processing and correlation analysis of digital images. …”
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Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning by ZhanQiang Xie, YongLi Situ, Li Deng, Meng Liang, Hang Ding, Zhen Guo, QinYing Xu, Zhu Liang, Zheng Shao

“…This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating differential gene expression analysis, weighted gene co-expression network analysis, Mfuzz clustering, single-cell RNA sequencing, and machine learning algorithms including LASSO regression, SVM-RFE, and random forest, five hub genes related to AD, including PLCB1, NDUFAB1, KRAS, ATP2A2, and CALM3 were identified. …”
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Analysis of the Strength Development, CO₂ Emissions, and Optimized Low-Carbon Design of Fly-Ash-Enhanced Composite Concrete by Bo Yang, Yu Dong, Xiao-Yong Wang

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Inflammation-Driven Prognosis in Advanced Heart Failure: A Machine Learning-Based Risk Prediction Model for One-Year Mortality by Zhou M, Du X

“…Data were split into training and validation sets. Seven ML algorithms were applied to build and evaluate models. …”
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Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer by Qingyu Xu, Ling Ma, Alexander Streuer, Eva Altrock, Nanni Schmitt, Felicitas Rapp, Alessa Klär, Verena Nowak, Julia Obländer, Nadine Weimer, Iris Palme, Melda Göl, Hong-hu Zhu, Wolf-Karsten Hofmann, Daniel Nowak, Vladimir Riabov

“…Abstract Background Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. …”
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Artificial Intelligence in Healthcare: Medical Students' Perspectives on Balancing Innovation, Ethics, and Patient-Centered Care by Eleanor Roy, Sara Malafa, Lina M. Adwer, Houda Tabache, Tanishqa Sheth, Vasudha Mishra, Moaz Elsayed Abouelmagd, Andrea Cushieri, Sajjad Ahmed Khan, Mihnea-Alexandru Gaman, Juan C. Puyana, Francisco Javier Bonilla-Escobar

“…However, this transformation also introduces pressing ethical challenges, particularly concerning autonomy, algorithmic bias, and data privacy. In this editorial, we explore these issues through the lens of medical students and future physicians, emphasizing the need for ethical vigilance and proactive governance in the deployment of AI technologies in clinical settings. …”
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Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research by Sobia Iftikhar, Sania Bhatti, Zulfiqar Ali Bhatti, Mohsin Ali Memon, Faisal Memon

“…To predict the arsenic value and cancer risk for the next five years, we have developed two models via Microsoft Azure machine learning with algorithms include Support Vector Machine (SVM), Linear Regression (LR), Bayesian Linear Regression (BLR), Boosted Decision tree (BDT), exponential smoothing ETS, Autoregressive Integrated Moving Average (ARIMA). …”
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Perspectives on Managing AI Ethics in the Digital Age by Lorenzo Ricciardi Celsi, Albert Y. Zomaya

“…Ultimately, it advocates for a global, transdisciplinary approach to AI governance that integrates legal, ethical, and technical perspectives, ensuring AI serves humanity while upholding democratic values and social justice. …”
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Hyperspectral imaging combined with residual-attention-net for spectral-spatial feature fusion in liver disease diagnosis by Yunze Li, Jingjing Wang, Miaoqing Zhao, Jinlin Deng, Chongxuan Tian, Qize Lv, Yifei Liu, Kun Ru, Wei Li

“…Sensitivity and specificity values also indicated strong diagnostic performance. …”
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Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (Ne) values. …”
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Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks by Ihor Blinov, Virginijus Radziukynas, Pavlo Shymaniuk, Artur Dyczko, Kinga Stecuła, Viktoriia Sychova, Volodymyr Miroshnyk, Roman Dychkovskyi

“…Given the presence of anomalies and missing values in the operational data, a two-stage preprocessing algorithm incorporating DBSCAN clustering was applied for data cleansing and imputation. …”
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