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A Layered Software Architecture for the Development of Smart Mobile Distributed Systems Oriented to the Management of Emergency Cases

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APPLIED SCIENCES-BASEL 2025年第7期15卷 3664-3664页

作者： Contreras Rivas, Lizbeth Yesenia Lopez Dominguez, Eduardo Hernandez Velazquez, Yesenia Dominguez Isidro, Saul Medina Nieto, Maria Auxilio de la Calleja, Jorge Lab Nacl Informat Avanzada Xalapa 91100 Veracruz Mexico Inst Politecn Nacl Ctr Invest & Estudios Avanzados Dept Comp Mexico City 07360 Mexico Univ Veracruzana Fac Stat & Informat Veracruz 91020 Mexico Univ Politecn Puebla Postgrad Dept Puebla 72640 Mexico

Smart mobile distributed systems (SMDSs) oriented to managing emergency cases (ECs) have become a helpful support tool in case of earthquakes at government and higher education institutions (GHEIs). This fact has motivated various works to propose SMDSs that are focused on managing ECs;however, many fail to address all essential requirements for effective managing and streamlining of the activities that must be carried out before, during, and after an earthquake occurs at GHEIs, resulting in suboptimal design decisions. In addition, none of these works proposes a reference software architecture that guides the development of these systems. That has given rise to issues, such as lack of standardization and low quality of the developed systems. This work presents a software architecture to develop SMDSs focused on managing the activities of people and members of the brigades in case of earthquakes at GHEIs. The proposed software architecture was evaluated based on four main aspects: correctness and coverage of desirable requirements, functional suitability, usability assessment, and validation and verification of the reference architecture elements. The results show that our software architecture provides suitable architectural support for developing SMDSs oriented to managing ECs, which meets the needed requirements.

关键词： software architecture indoor location classification algorithm brigade activities web system mobile application

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Machine learning framework for selective and sensitive metal ion sensing with nitrogen-doped graphene quantum dots heterostructure

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CARBON 2025年 232卷

作者： Das, Ruma Paria, Abhirup Giri, P. K. Haldia Inst Technol Dept Appl Sci Phys Haldia 721657 India Haldia Inst Technol Dept Comp Sci & Engn Artificial Intelligence & Mac Haldia 721657 India Indian Inst Technol Guwahati Dept Phys Gauhati 781039 India Indian Inst Technol Guwahati Ctr Nanotechnol Gauhati 781039 India

This study introduces a machine learning (ML) framework to optimize photodetector performance for sensor applications. Using the data from the fabricated photodetector with the heterostructure of nitrogen-doped graphene quantum dot and gold nanoparticles (Au@N-GQDs), various supervised ML models (more than 20 models) are trained and tested for the selection and refinement of the most effective algorithm for our work. Depending on the best-performed ML model, the optimized working wavelength of the photodetector is found for the detection of metal ions. Remarkably, the ML-based sensor shows a high level of selectivity and sensitivity in nM level towards Fe3+ ions in Brahmaputra river water. A strong alignment between model predictions and experimental outcomes validates the efficacy of the proposed ML-based framework. Moreover, data visualization techniques such as heatmaps, classification algorithms, and confusion matrices are introduced to identify the trends in the database. The mechanistic insight of the sensor performance towards Fe3+ ion sensing is further explained with heatmap analysis and experimental verification, which emphasizes the role of photo-induced charge transfer and Fe-O bond formation between metal ions and Au@N-GQDs due to the high electron affinity of Fe3+ ions.

关键词： Supervised machine learning Heatmap matrix classification algorithm Confusion matrix Metal ion sensing in river water

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Application of Big Data Technology to Assessments of Female Ovarian Reserve Dysfunction

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IEEE ACCESS 2025年 13卷 19408-19419页

作者： Xia, Ji'An Ma, Yunfei Wu, Yiyun Zhao, Youlin Ni, Haorang Liu, Xinyan Nanjing Vocat Univ Ind Technol Sch Comp & Software Nanjing 210023 Jiangsu Peoples R China Jiangsu Prov Hosp Chinese Med Dept Ultrasound Nanjing 210029 Jiangsu Peoples R China

This paper evaluated the potential application of big data technology to assessments of diminished ovarian reserve (DOR). The study enrolled 162 patients who underwent ovarian reserve function assessment for the first time in the Department of Ultrasound, Jiangsu Province Hospital of Chinese Medicine from January 2023 to December 2023. Patients were divided into normal ovarian reserve function (n = 68), early-stage DOR (n = 66), mid-stage DOR (n = 12), and late-stage DOR (n = 16) groups. Hadoop and Spark frameworks were used to build a big data platform, and the MLlib parallel machine learning library was used to implement three multivariate classification models-multilayer perceptron, one-vs-rest, and random forest classifiers-to classify and analyse the ovarian reserve function dataset and evaluate the platform's performance. In the big data platform, the random forest algorithm achieved the highest classification accuracy (89.47%), followed by the neural network (81.06%) and support vector machine (72.91%) methods. The random forest algorithm had the least time overhead for datasets smaller than 50 MB;for datasets exceeding 50 MB, the support vector machine algorithm had the least time overhead, followed by the random forest and neural network algorithms. The neural network algorithm's speedup ratio was lower than that of the other two algorithms for small datasets, but with increasing dataset size, its speedup ratio significantly exceeded those of the other two algorithms. The random forest algorithm showed substantial growth for large datasets.

关键词： Medical diagnostic imaging Big Data Machine learning algorithms Sparks classification algorithms Indexes Data models Random forests Biochemistry Support vector machines Diminished ovarian reserve three-dimensional power Doppler ultrasound medical big data machine learning classification algorithm performance evaluation

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A Review of Machine Learning in Organic Solar Cells

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PROCESSES 2025年第2期13卷 393-393页

作者： Ahmed, Darya Rasul Muhammadsharif, Fahmi F. Koya Univ Fac Sci & Hlth Dept Phys Koya KOY45 Koy Sanjaq Iraq

Organic solar cells (OSCs) are a promising renewable energy technology due to their flexibility, lightweight nature, and cost-effectiveness. However, challenges such as inconsistent efficiency and low stability limit their widespread application. Addressing these issues requires extensive experimentation to optimize device performance, a process hindered by the complexity of OSC molecular structures and device architectures. Machine learning (ML) offers a solution by accelerating material discovery and optimizing performance through the analysis of large datasets and prediction of outcomes. This review explores the application of ML in advancing OSC technologies, focusing on predicting critical parameters such as power conversion efficiency (PCE), energy levels, and absorption spectra. It emphasizes the importance of supervised, unsupervised, and reinforcement learning techniques in analyzing molecular descriptors, processing data, and streamlining experimental workflows. Concludingly, integrating ML with quantum chemical simulations, alongside high-quality datasets and effective feature engineering, enables accurate predictions that expedite the discovery of efficient and stable OSC materials. By synthesizing advancements in ML-driven OSC research, the gap between theoretical potential and practical implementation can be bridged. ML can viably accelerate the transition of OSCs from laboratory research to commercial adoption, contributing to the global shift toward sustainable energy solutions.

关键词： machine learning organic solar cell feature selection photovoltaic parameter classification algorithm

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RAPID DETECTION OF COMMON ADDITIVES IN TEA FOR QUALITY ASSURANCE VIA MID-INFRARED SPECTROSCOPY AND MACHINE LEARNING

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ACTA SCIENTIARUM POLONORUM-TECHNOLOGIA ALIMENTARIA 2025年第1期24卷 27-45页

作者： Liu, Weiyu Lin, Yuduan Liu, Cuihua Cai, Honghao Ni, Hui Jimei Univ Sch Sci Dept Phys Xiamen Fujian Peoples R China Guilin Univ Technol Sch Mat Sci & Engn Guilin Guangxi Peoples R China Xiamen Univ Sch Elect Sci & Engn Xiamen Fujian Peoples R China Jimei Univ Coll Food & Biol Engn Xiamen Fujian Peoples R China Fujian Prov Key Lab Food Microbiol & Enzyme Engn Xiamen Fujian Peoples R China

Background. Tea, a globally popular beverage, is frequently adulterated with various additives for profit. However, research on these additives - particularly their common combinations in fraudulent practices - remains limited. Materials and methods. This study uses mid-infrared (MIR) spectroscopy and machine learning techniques to identify such additives in tea. We trained several models - AdaBoost, random forest, K-nearest neighbours (KNN), support vector classification, Gaussian naive Bayes, and decision tree - to detect adulterants such as white sugar, talcum powder, sulphur, paraffin wax, food colouring, and flavourings, as well as their combinations. To improve accuracy and efficiency, we employed the Successive Projections algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) for feature selection, prioritising features that exhibited the strongest correlations with the additives. Results. Compared to models relying solely on raw spectra, those incorporating SPA and CARS consistently achieved high accuracy and reduced detection time by at least 90%. The selected wavenumbers also serve as biomarkers for specific additives. The SPA-KNN model performed exceptionally well, with an accuracy of 0.956, macro-precision of 0.964, macro-recall of 0.956, and a macro-F1 score of 0.956, all with a detection time of just 0.9 seconds. Conclusion. These results highlight the effectiveness of combining MIR spectroscopy with advanced classifiers and feature selection algorithms, offering a rapid and precise method for detecting tea additives. This approach benefits producers, distributors, and consumers alike.

关键词： adulteration food quality K-nearest neighbours classification algorithm feature selection

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A parallel and distributed C4.5 algorithm in cloud computing environments

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COMPUTING 2025年第2期107卷 1-38页

作者： Lin, Kawuu W. Zheng, Ya-Jun Chen, Ju-Chin Wang, Wei-Chiang Chen, Chao-Chun Natl Kaohsiung Univ Sci & Technol Dept Comp Sci & Informat Engn Jiangong Rd Kaohsiung 807618 Taiwan Natl Cheng Kung Univ Inst Mfg Informat & Syst Univ Rd Tainan 70101 Taiwan

Data mining seeks to derive significant insights from big data, offering valuable information for decision-makers and generating economic value. Among its tasks, classification is one of the most essential, with decision tree algorithms being a widely adopted solution due to their efficiency in addressing association rules and classification challenges. Decision trees are particularly advantageous for their ability to provide interpretable results at minimal computational cost. However, the exponential growth of data in the Internet era has highlighted the limitations of traditional algorithms, which struggle to efficiently process large-scale *** address this issue, this study introduces PD-C4.5, a parallel and distributed implementation of the C4.5 algorithm designed for cloud computing environments. By incorporating a microservices architecture, the proposed approach modularizes computation, enabling flexible, scalable, and distributed execution. This design not only enhances system maintainability but also optimizes resource utilization while maintaining high computational *** evaluations demonstrate that PD-C4.5 significantly outperforms Original C4.5 and MR-C4.5 in handling medium and large datasets, achieving notable reductions in computation time and resource consumption. Additionally, the integration of microservices ensures seamless scalability to accommodate increasing data volumes. This study provides a novel and practical solution for large-scale data classification, bridging the gap between computational efficiency and scalability in the era of big data.

关键词： Data mining classification algorithm C4.5 Distributed mining

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Use of Machine Learning algorithms to Study the Hospitalization for Coronary Artery Bypass at University Hospital "Federico II" 6th

Use of Machine Learning Algorithms to Study the Hospitalizat...

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6th International Conference on Biomedical Engineering

作者： Marino, Marta Rosaria Borrelli, Anna Triassi, Maria Improta, Giovanni Univ Naples Federico II Dept Publ Hlth Naples Italy Federico II Univ Hosp Naples Italy Univ Naples Federico II Interdept Ctr Res Healthcare Management & Innovat Naples Italy

ISBN: (纸本)9783031803543;9783031803550

Coronary artery bypass graft (CABG) is a surgical treatment performed to treat coronary artery disease. During CABG, the patient's coronary arteries are cleaned out of obstructions and new, heart-blood-supplying arteries are implanted in their place. Following the CABG, the objective of postoperative care is aimed at fostering the patient's recovery by reducing the risk of complications. Given that a prolonged length of stay may result in a significant financial burden and ineffective management of hospital resources, the length of stay (LOS) in the hospital is an important component for a good organizational structure. The LOS of patients receiving bypass surgery has been predicted in this study using Machine Learning (ML) techniques. Computer systems can automatically learn from data without explicit programming thanks to a combination of mathematical models and statistical techniques called machine learning (ML). The dataset comprises 243 patients having heart bypass surgery in 2019-2020, was obtained from the University Hospital "Federico II" - Complex Operative Unit (C.O.U.) of Cardiology. The data were compared with those obtained from two other hospitals (University Hospital "San Giovanni di Dio e Ruggi d'Aragona" and those of the national hospital "Antonio Cardarelli") to identify the algorithm with the best performance in predicting length of stay of patient undergoing CABG by analyzing personal and clinical information like age, presence/absence of comorbidities.

关键词： Coronary Artery Bypass Machine Learning classification algorithm Length of Hospital Stay

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Learning rates of gradient descent algorithm for classification

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2009年第1期224卷 182-192页

作者： Dong, Xue-Mei Chen, Di-Rong Beijing Univ Aeronaut & Astronaut Dept Math Beijing 100083 Peoples R China Minist Educ LMIB Beijing 100083 Peoples R China

In this paper, a stochastic gradient descent algorithm is proposed for the binary classification problems based on general convex loss functions. It has computational superiority over the existing algorithms when the sample size is large. Under some reasonable assumptions on the hypothesis space and the underlying distribution, the learning rate of the algorithm has been established, which is faster than that of closely related algorithms. (C) 2008 Elsevier B.V. All rights reserved.

关键词： classification algorithm Stochastic gradient descent Reproducing kernel Hilbert space Learning rates computational complexity

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Multivariate control charts that combine the Hotelling T² and classification algorithms

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2019年第6期70卷 889-897页

作者： Park, Sung Ho Kim, Seoung Bum Korea Univ Sch Ind Management Engn Seoul South Korea

Multivariate control charts, including Hotelling's T-2 chart, have been widely adopted for the multivariate processes found in many modern systems. However, traditional multivariate control charts assume that the in-control group is the only population that can be used to determine a decision boundary. However, this assumption has restricted the development of more efficient control chart techniques that can capitalise on available out-of-control information. In the present study, we propose a control chart that improves the sensitivity (i.e., detection accuracy) of a Hotelling's T-2 control chart by combining it with classification algorithms, while maintaining low false alarm rates. To the best of our knowledge, this is the first attempt to combine classification algorithms and control charts. Simulations and real case studies demonstrate the effectiveness and applicability of the proposed control chart.

关键词： Quality control statistical process control multivariate control chart classification algorithm

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Online regularized classification algorithms

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IEEE TRANSACTIONS ON INFORMATION THEORY 2006年第11期52卷 4775-4788页

作者： Ying, Yiming Zhou, Ding-Xuan City Univ Hong Kong Dept Math Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Math Kowloon Hong Kong Peoples R China

This paper considers online classification learning algorithms based on regularization schemes in reproducing kernel Hilbert spaces associated with general convex loss functions. A novel capacity independent approach is presented. It verifies the strong convergence of the algorithm under a very weak assumption of the step sizes and yields satisfactory convergence rates for polynomially decaying step sizes. Explicit learning rates with respect to the misclassification error are given in terms of the choice of step sizes and the regularization parameter (depending on the sample size). Error bounds associated with the hinge loss, the least square loss, and the support vector machine q-norm loss are presented to illustrate our method.

关键词： classification algorithm error analysis online learning regularization reproducing kernel Hilbert spaces

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