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Trace detection of antibiotics in wastewater using tunable core-shell nanoparticles SERS substrate combined with machine learning algorithms

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025年 330卷 125700页

作者： Usman, Muhammad Ali, Wajid Alarfaji, Saleh S. Tamulevicius, S. Kaunas Univ Technol Inst Mat Sci K Barsausko St 59 LT-51423 Kaunas Lithuania Kaunas Univ Technol Dept Phys Studentu St 50 LT-51423 Kaunas Lithuania Hunan Univ Coll Mat Sci & Engn Key Lab Micronano Phys & Technol Changsha 410082 Hunan Peoples R China King Khalid Univ Fac Sci Dept Chem POB 9004 Abha 61413 Saudi Arabia

Surface-enhanced Raman scattering (SERS) show great potential for rapid and highly sensitive detection of trace amounts of contamination from the environment in the surface aquatic ecosystem. The widespread use of antibiotics has resulted in serious degradation of the water environment in the past few years, and their substantial residual contamination of wastewater has a harmful effect on ecosystems, which is associated with the development of antibiotic-resistant bacterial strains. However, in this study, a novel approach of core-shell nano- particles GNRs@1,4-BDT@Ag was used for the quantitative measurement of the concentration of antibiotics in wastewater solutions using the SERS technique coupled with computational methods. In our experiments, we selected commonly used antibiotics such as ciprofloxacin and levofloxacin in wastewater solutions. We then obtained SERS spectra for each antibiotic and its various combinations at varying concentrations. We combined it with machine learning algorithms to accurately identify and quantify the SERS spectra of the residual antibiotics in the system. Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were subsequently employed for clustering analysis of the SERS spectral datasets. To evaluate the performance of machine learning algorithms five metrics were applied. The classification results demonstrate that while most algorithms achieved over 95 % accuracy in antibiotics status prediction, the Support Vector machine (SVM) model had the best performance, attaining a remarkable prediction accuracy of up to 99 %. This developed approach helps as a simple and expeditious tool for the analysis of antibiotics in wastewater and exhibits potential for broader applications in various domains.

关键词： Surface-enhanced Raman spectroscopy machine learning algorithms GNRs@Ag core-shell nanoparticles Antibiotics Detection sensitivity

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Application of social entertainment robots based on machine learning algorithms and the Internet of Things in collaborative art performances

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Zhao, Zhenhua Guo, Feng Sangmyung Univ Dept Hightech Arts Convergence Seoul 03016 South Korea Taiyuan Univ Technol Dept Informat Engn Taiyuan 030024 Shanxi Peoples R China

In terms of control strategies for social entertainment robots, advanced control system design was adopted in the study, aiming to enable robots to achieve efficient collaborative art performances. The control system is based on machine learning algorithms and Internet of Things technology, combined with the application of sensing technology, providing accurate environmental perception and real-time feedback mechanisms for robots. Considering the collaboration and interaction between robots and human actors, control strategies adapted to different scenes were designed by analyzing and understanding the needs of artistic performances. These strategies not only consider the robot's own actions and performance, but also the interaction with human actors and the coordination with the entire performance scene. The control method in this article combines machine learning algorithms and sensing technology to enable robots to make intelligent decisions and action planning by learning and perceiving real-time environmental information. By modeling and simulating the structure and characteristics of the robot, precise planning and control of the robot's motion trajectory can be achieved. Through dynamic modeling, it is possible to better understand the motion characteristics and energy consumption of robots, and to adjust and optimize their actions during the performance process.

关键词： machine learning algorithms Internet of Things Social entertainment robots Collaborative performance Art performance

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Determining the most accurate machine learning algorithms for medical diagnosis using the monk' problems database and statistical measurements

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2025年第2期37卷 357-376页

作者： Avuclu, Emre Aksaray Univ Dept Software Engn Fac Engn Aksaray Turkiye

Computer-aided diagnosis process in the field of health, especially cancer diagnosis, is of vital importance. Computer-aided diagnosis helps specialist physicians to make the most accurate diagnosis. According to research studies, it has been stated that the number of wrong or late diagnosis increases with each passing year and ultimately causes the death of people living in many parts of the world. For this reason, some calculations must be made to determine the most accurate one in the algorithm to be used to make the correct diagnosis. In this study, three different database Monk' problems were used to determine the most accurate algorithm for medical diagnosis. Monk' problems are used as one of the several classification problems used to create an important comparative study. Train and test operations were performed using five different machine learning algorithms (MLAs) (k Nearest Neighbor (k-NN), Decision Tree Algorithm (DT), Random Forest Algorithm (RF), Naive Bayes algorithm (NB), Support Vector Cases (SVM)). These machine learning algorithms are compared statistically in terms of performance. Two different databases in the medical field were used to test the results (Breast Cancer Coimbra Data Set, Diabetic Retinopathy Debrecen Data Set). In the test processes in the experimental studies, the highest accuracy rate was obtained from the k-NN, DT, RF, NB, SVM algorithms, respectively;0.9758, 1, 1, 0.9180, 0.9344. The best performance was obtained from RF MLA for 1. dataset, DT MLA for 2. dataset, highest accuracy rates from k-NN and RF MLAs in 3. dataset.

关键词： The most accurate diagnosis Monk' problems Cancer diagnosis statistical measurements machine learning algorithms

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Neuromorphic devices assisted by machine learning algorithms

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INTERNATIONAL JOURNAL OF EXTREME MANUFACTURING 2025年第4期7卷 042007-042007页

作者： Huo, Ziwei Sun, Qijun Yu, Jinran Wei, Yichen Wang, Yifei Cho, Jeong Ho Wang, Zhong Lin Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 101400 Peoples R China Univ Chinese Acad Sci Sch Nanosci & Engn Beijing 100049 Peoples R China Guangxi Univ Ctr Nanoenergy Res Sch Phys Sci & Technol Nanning 530004 Peoples R China Shandong Zhongke Naneng Energy Technol Co Ltd Dongying Peoples R China Yonsei Univ Dept Chem & Biomol Engn Seoul 03722 South Korea Georgia Inst Technol Atlanta GA 30332 USA

Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided. The review discusses the basic structure of simple neuron models inspired by biological neurons and how they process information in simple neural networks, laying the foundation for neuromorphic device *** progress in the fabrication of neuromorphic devices is highlighted, focusing on advancements in materials, structures, and the development of st

关键词： neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors

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Two-way detection of tuberculosis bacilli using fano resonance sensor and machine learning algorithms

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MATERIALS RESEARCH EXPRESS 2025年第3期12卷 035003-035003页

作者： Singh, Pooja Singh, Lokendra Yadav, Sameer Shekhar, Sudhanshu Bhalerao, Siddharth Rai, Preeti Pareek, Prakash Suri, Ajay SSBSR Sharda Univ Ctr Solar Cells & Renewable Energy CSRE Dept Phys Greater Noida 201310 India Graph Era Dept ECE Dehra Dun 248002 Uttarakhand India Gyan Ganga Inst Technol & Sci Dept CSE Jabalpur 482003 MP India Vishnu Inst Technol Dept ECE Bhimavaram 534202 Andhra Pradesh India ABES Engn Coll Dept ECE Ghaziabad 201009 Uttar Pradesh India

A serious worldwide health concern, tuberculosis (TB) necessitates creative methods for quick and precise diagnosis. This paper presents the detection of Mycobacterium tuberculosis (mTB) using a two-way approach utilizing refractive index (RI) sensors and machine learning (ML) techniques. The approach is validated by adopting a Fano resonance (FR) sensor which consists of freshly prepared cerium oxide nanostructures (CNS) covered sensing slots. The CNS is prepared by using combustion technique and characterized through transmission electron microscopy (TEM) and x-ray diffraction (XRD). The RI values sensed through the sensor yield a high sensitivity and autocorrelation coefficient of 163 nm RIU-1 and R2=92.37% , respectively. The microscopic sputum smear examination is used to detect mTB through five different ML classifiers. Among all, the random forest classifier provides the highest accuracy of 96%. According to preliminary findings, the suggested system provides a quick, affordable, and non-invasive diagnostic option with excellent accuracy, sensitivity, and specificity. The approach addresses important TB diagnostic problems and is more suitable for low-resource environments. This hybrid strategy can transform TB detection, enhance patient outcomes, and support TB control initiatives.

关键词： fano resonance mycobacterium tuberculosis two-way detection machine learning algorithms

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Predicting the risk of a high proportion of three/multiple pronuclei (3PN/ MPN) zygotes in individual IVF cycles using comparative machine learning algorithms

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EUROPEAN JOURNAL OF OBSTETRICS & GYNECOLOGY AND REPRODUCTIVE BIOLOGY 2025年 306卷 139-146页

作者： Hong, Pingping Lu, Yaxin Li, Haiyang Liu, Zifeng Ou, Jianpin Li, Tao Shu, Yimin Sun Yat Sen Univ Affiliated Hosp 3 Reprod Med Ctr 6 East Longkou Rd Guangzhou 510630 Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Ctr Big Data & Artificial Intelligence 600 Tianhe Rd Guangzhou Guangdong Peoples R China Univ Kansas Med Ctr Ctr Adv Reprod Med Dept Obstet & Gynecol Overland Pk KS 66211 USA

Background: The majority of machine learning applications in assisted reproduction have been focused on predicting the likelihood of pregnancy. In the present study, we aim to investigate which machine learning models are most effective in predicting the occurrence of a high proportion (>30 %) of 3PN/MPN zygotes in individual IVF cycles. Methods: Eight machine learning algorithms were trained and compared, including the AdaBoost and Gaussian NB. Data from IVF cycles carried out from September 2015 to September 2019 were used as a training set. Cycle data from October 2019 to June 2020 were used as a validation set to verify the training model. Cycles with a 3PN/MPN zygote proportion higher than 30 % were classified as high 3PN/MPN zygote proportion cycles. Results: The AdaBoost algorithm was the best model for model construction and external validation. In both the training and validation sets, age, basal FSH, FSH and E2 level on the day of Gonadotrophin (GN) stimulation, and FSH and LH levels on the day of HCG were statistically higher in patients with 3PN/MPN > 30 % than in patients with 3PN/MPN <= 30 %;AFC, AMH, E2 level on HCG day and total number of oocytes were lower in patients with 3PN/MPN > 30 % than in patients with 3PN/MPN <= 30 %. The top five predictors were the number of oocytes retrieved, age, male factor infertility, AFC, and total days of GN stimulation. Conclusion: By applying a suitable machine learning algorithm, we can potentially predict the risk of a high proportion of 3PN/MPN zygotes in individual IVF cycles before insemination and avoid polyspermy fertilization by ICSI fertilization method.

关键词： Three/multiple pronuclei (3PN/MPN) zygotes machine learning algorithms AdaBoost algorithm IVF Abnormal fertilization

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Species distribution modeling of Malva neglecta Wallr. weed using ten different machine learning algorithms: An approach to site-specific weed management (SSWM)

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EUROPEAN JOURNAL OF AGRONOMY 2025年 167卷

作者： Dastres, Emran Esmaeili, Hassan Edalat, Mohsen Shahid Beheshti Univ Med Plants & Drugs Res Inst Dept Agr Tehran *** Iran Shiraz Univ Sch Agr Plant Prod & Genet Dept Shiraz Iran

The integration of artificial intelligence (AI) and machine learning algorithms (MLAs) has transformed ecological modeling and precision agriculture by improving the prediction of plant species distribution. This advancement is particularly significant in agricultural practices like weed management, where precision and sustainability are critical for enhancing farming efficiency and environmental conservation. The study conducted in Fars province, Iran was aimed to apply species distribution modeling (SDM) to site-specific weed management (SSWM) in rapeseed (Brassica napus L.) fields. The focus was on improving precision in control of the weed Malva neglecta, using advanced machine learning algorithms. The study employed machine learning algorithms, including Random Forest (RF), Boosted Regression Trees (BRT), Support Vector machine (SVM), Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), k-Nearest Neighbors (KNN), Generalized Additive Model (GAM), Generalized Boosting Model (GBM), Classification and Regression Trees (CART), and Multivariate Adaptive Regression Splines (MARS) to predict the distribution of M. neglecta. Geographic Information Systems (GIS) were used to create raster maps from eleven environmental factors for spatial analysis. A multi-collinearity test using the variance inflation factor (VIF) ensured model robustness, and factor importance was analyzed with Boruta, RF, Generalized Linear Model (GLM), and Partial Least Squares (PLS) algorithms. The study found that machine learning algorithms, particularly RF, CART, MARS, and BRT, provided highly accurate predictions of M. neglecta distribution. The analysis identified mean annual rainfall, slope, and silt percentage as key environmental factors influencing the distribution of M. neglecta. The successful application of these algorithms in predicting weed distribution demonstrates their potential for improving ecological modeling. The study highlights the effectiveness of SDM i

关键词： Data usage technique Environmental factors Spatial modeling machine learning algorithms Malva neglecta Weed distribution

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An efficient strategy for early sex identification in Litsea cubeba based on portable Raman technology combined with machine learning algorithms

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2025年 140卷

作者： Wang, Chuyi Xu, Peipei Wang, Haonan Luo, Feili Tu, Qianmin Fang, Yulin You, Ruiyun Yang, Yan Lu, Yudong Fujian Normal Univ Coll Chem & Mat Sci Fujian Prov Key Lab Adv Oriented Chem Engineer Fujian Key Lab Polymer Mat Fuzhou 350117 Fujian Peoples R China Fujian Normal Univ Coll Life Sci Fuzhou 350117 Peoples R China Hunan Acad Forestry State Key Lab Utilizat Woody Oil Resource Changsha 410004 Peoples R China

Early sex identification of the dioecious and medicinal spice plant, Litsea cubeba (Lour.) Pers (LC), is necessary for expanding the production and application of LC. We describe a Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) method combined with machine learning techniques. Based on the RS and SERS features, we combined principal component analysis and linear discriminant analysis (PCA-LDA) to dimension reduction for the data. And we combined seven machine learning algorithms, including logistic regression (LR), Naive Bayes (NB), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), support vector machines (SVM), and extreme gradient boosting (XGBoost) algorithms to construct a gender prediction model for LC. The results demonstrated that the surface reinforcement treatment with drops of silver sol combined with LR, SVM, and XGBoost models achieved an accuracy of 84.62 % in distinguishing male and female leaves. Furthermore, compared to the discrimination effect of scanning the leaf surface only, the gender recognition accuracy of the surface reinforcement treatment increased by 17.31 %, 19.24 % and 19.24 %, respectively. Portable Raman spectroscopy combined with machine learning algorithms can be promoted for use as a tool for early sex identification in most plants, which can be applied to large-scale plant cultivation and breeding programmes.

关键词： Litsea cubeba Surface-enhanced Raman spectroscopy Sex identification Leaves machine learning algorithms

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Enhancing the diagnostic accuracy of colorectal cancer through the integration of serum tumor markers and hematological indicators with machine learning algorithms

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CLINICAL & TRANSLATIONAL ONCOLOGY 2025年第1期27卷 299-308页

作者： Xu, Rongxuan Chi, Huimin Zhang, Qian Li, Xiaofeng Hong, Zhijun Dalian Med Univ Dept Epidemiol & Hlth Stat Dalian Peoples R China Dalian Med Assoc Dian Peoples R China Dalian Med Univ Hlth Management Ctr Affiliated Hosp 1 Dalian Liaoning Peoples R China

BackgroundColorectal cancer has a high incidence and mortality rate due to a low rate of early diagnosis. Therefore, efficient diagnostic methods are urgently *** This study assesses the diagnostic effectiveness of Carbohydrate Antigen 19-9 (CA19-9), Carcinoembryonic Antigen (CEA), Alpha-fetoprotein (AFP), and Cancer Antigen 125 (CA125) serum tumor markers for colorectal cancer (CRC) and investigates a machine learning-based diagnostic model incorporating these markers with blood biochemical indices for improved CRC *** Between January 2019 and December 2021, data from 800 CRC patients and 697 controls were collected;52 patients and 63 controls attending the same hospital in 2022 were collected as an external validation set. Markers' effectiveness was analyzed individually and collectively, using metrics like ROC curve AUC and F1 score. Variables chosen through backward regression, including demographics and blood tests, were tested on six machine learning models using these *** the case group, the levels of CEA, CA199, and CA125 were found to be higher than those in the control group. Combining these with a fourth serum marker significantly improved predictive efficacy over using any single marker alone, achieving an Area Under the Curve (AUC) value of 0.801. Using stepwise regression (backward), 17 variables were meticulously selected for evaluation in six machine learning models. Among these models, the Gradient Boosting machine (GBM) emerged as the top performer in the training set, test set, and external validation set, boasting an AUC value of over 0.9, indicating its superior predictive *** learning models integrating tumor markers and blood indices offer superior CRC diagnostic accuracy, potentially enhancing clinical practice.

关键词： Colorectal cancer diagnosis Serum tumor markers machine learning algorithms Blood biochemical indices Diagnostic model optimization

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Application and optimization of machine learning algorithms for optical character recognition in complex scenarios

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JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期34卷

作者： Liu, Liming Yang, Dexin Chen, Juntao Guangzhou City Polytech Sch Informat Engn Guangzhou 510405 Guangdong Peoples R China Guangzhou City Polytech Informat & Educ Technol Ctr Guangzhou 510405 Guangdong Peoples R China

In the era of artificial intelligence, the technology of optical character recognition under complex backgrounds has become particularly important. This article investigated how machine learning algorithms can improve the accuracy of text recognition in complex scenarios. By analyzing algorithms such as scale-invariant feature transform, K-means clustering, and support vector machine, a system was constructed to address the challenges of text recognition under complex backgrounds. Experimental results show that the proposed algorithm achieves 7.66% higher accuracy than traditional algorithms, and the built system is fast, powerful, and highly satisfactory to users, with a 13.6% difference in results between the two groups using different methods. This indicates that the method proposed in this study can effectively meet the needs of complex text recognition, significantly improving recognition efficiency and user satisfaction.

关键词： automated machine learning algorithms artificial intelligence machine learning algorithms optical character recognition system implementation

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