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

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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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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Performance evaluation of different machine learning algorithms for prediction of nitrate in groundwater in Thiruvannamalai District

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CLEAN-SOIL AIR WATER 2025年第1期53卷

作者： Jacob, Christina Masilamani, Uma Shankar Vellore Inst Technol Sch Civil Engn Dept Environm & Water Resources Engn Vellore 632014 India

The prevalence of nitrates (NO3-) in groundwater due to the extensive application of fertilizers and anthropogenic sources pollutes the groundwater. machine learning (ML) techniques are now being increasingly deployed to achieve high precision in predicting water quality. This study assesses the efficacy of nine distinct ML algorithms, namely, linear regression, polynomial regression, decision tree, random forest (RF), support vector machine, multilayer perceptron regressor, eXtreme gradient boosting (XGB), light gradient boosting (LGB), and K-nearest neighbors to predict nitrate concentration in the groundwater in Thiruvannamalai District, Tamil Nadu. Overall, 360 water samples for 1 year and 14 groundwater variables were determined to predict nitrate. Performance evaluation metrics such as root mean square error (RMSE), moving average error (MAE), and correlation coefficient (R2) were evaluated for pre-monsoon, monsoon, and post-monsoon seasons. For all three seasons, RF predicted the nitrate concentration with low values of RMSE, MAE, and higher values of R2. The results show values for RF with: RSME: 0.49, MAE: 1.30, and R2: 0.94, which has a higher prediction tailed by LGB and XGB and is true for all the seasons. The results from the study will aid the policymakers in planning the strategy for remediation. Graphical Abstract: Main sources of nitrate contamination are agricultural fertilizers, industrial discharges, septic tanks, landfills, and animal feedlots. GIS was used to analyze the spatial distribution for each of the three seasons, pre-monsoon, monsoon, and post-monsoon, in Polur Taluk. Nine distinct machine learning algorithms were evaluated to predict nitrate and are presented in a scatterplot. image

关键词： groundwater machine learning algorithms nitrate contamination prediction RMSE

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Advancements in Circulating Tumor Cell Detection for Early Cancer Diagnosis: An Integration of machine learning algorithms with Microfluidic Technologies

BIOSENSORS-BASEL

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BIOSENSORS-BASEL 2025年第4期15卷 220-220页

作者： An, Ling Liu, Yi Liu, Yaling Dali Univ Sch Engn Dali 671003 Peoples R China Sichuan Univ West China Hosp Precis Med Translat Res Ctr Chengdu 610041 Peoples R China Lehigh Univ Dept Bioengn Bethlehem PA 18015 USA

Circulating tumor cells (CTCs) are vital indicators of metastasis and provide a non-invasive method for early cancer diagnosis, prognosis, and therapeutic monitoring. However, their low prevalence and heterogeneity in the bloodstream pose significant challenges for detection. Microfluidic systems, or "lab-on-a-chip" devices, have emerged as a revolutionary tool in liquid biopsy, enabling efficient isolation and analysis of CTCs. These systems offer advantages such as reduced sample volume, enhanced sensitivity, and the ability to integrate multiple processes into a single platform. Several microfluidic techniques, including size-based filtration, dielectrophoresis, and immunoaffinity capture, have been developed to enhance CTC detection. The integration of machine learning (ML) with microfluidic systems has further improved the specificity and accuracy of CTC detection, significantly advancing the speed and efficiency of early cancer diagnosis. ML models have enabled more precise analysis of CTCs by automating detection processes and enhancing the ability to identify rare and heterogeneous cell populations. These advancements have already demonstrated their potential in improving diagnostic accuracy and enabling more personalized treatment approaches. In this review, we highlight the latest progress in the integration of microfluidic technologies and ML algorithms, emphasizing how their combination has changed early cancer diagnosis and contributed to significant advancements in this field.

关键词： circulating tumor cells (CTCs) microfluidic systems machine learning algorithms early cancer diagnosis

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Prediction of late-onset depression in the elderly Korean population using machine learning algorithms

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Park, Jong Wan Ko, Chang Woo Lee, Diane Youngmi Kim, Jae Chul Hannam Univ Grad Sch Dept Counseling 70 Hannam Ro Daejeon 34430 South Korea Seoul Natl Univ Coll Med Dept Biomed Sci 103 Daehak Ro Seoul 03080 South Korea Daegu Cyber Univ Dept Art Therapy 201 Daegudae Ro Gyongsan 38453 South Korea

Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression. Using public data from the nationwide panel survey 'Korean Longitudinal Study of Aging,' we employed latent growth modeling and growth mixture modeling to identify four latent classes of depression trajectories in the elderly Korean population. Based on the results of binary logistic regression, we selected 12 variables capable of distinguishing the LOD population from the reference population and tested 12 machine learning (ML) algorithms. While most ML algorithms showed acceptable predictive capability, Random Forest Classifier and Gradient Boosting Classifier demonstrated superior performance. Consequently, we successfully established new ML-based LOD prediction programs. These programs could be further developed into self-checking online tools, expected to serve as decision support systems for primary medical care and health screening services.

关键词： Late-onset depression Longitudinal study of aging Depression trajectories machine learning algorithms Predictive performance

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An early prediction model for gestational diabetes mellitus created using machine learning algorithms

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INTERNATIONAL JOURNAL OF GYNECOLOGY & OBSTETRICS 2025年第0期 2025 Mar 4页

作者： Yang, Zhifen Shi, Xiaoyue Wang, Shengpu Du, Lijia Zhang, Xiaoying Zhang, Kun Zhang, Yongqiang Ma, Jinlong Zheng, Rui Hebei Med Univ Hosp 4 Obstetr Dept 169 Tianshan St Shijiazhuang 050000 Peoples R China Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang Hebei Peoples R China

ObjectiveTo investigate high-risk factors for gestational diabetes mellitus (GDM) in early pregnancy through an analysis of demographic and clinical data, and to develops a machine-learning-based prediction model to enhance early diagnosis and intervention. MethodsA retrospective study was performed involving 942 pregnant women. A stacking ensemble (machine learning [ML]) was applied to demographic and clinical variables, creating a predictive model for GDM. Model performance was evaluated through receiver-operating characteristics (ROC) analysis, and the area under the curve (AUC) was calculated. Risk stratification was performed using quartile-based probability thresholds, and predictive accuracy was validated using an independent dataset. ResultsSignificant predictors for GDM included age, pre-pregnancy body mass index (BMI;calculated as weight in kilograms divided by the square of height in meters), history of GDM, family history of diabetes, history of fetal macrosomia, education level, history of hypertension, and gravidity. These factors, which can be collected non-invasively at the first prenatal visit, formed the basis of a robust predictive model (AUC = 0.89). The model demonstrated a strong ability to exclude GDM, at a threshold of 28.53%. ConclusionsThe machine-learning-based prediction model effectively identifies populations at high risk for GDM before invasive testing and oral glucose tolerance test, facilitating early clinical intervention and resource optimization.

关键词： early prediction model gestational diabetes mellitus hierarchical structural analysis machine learning algorithms

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Groundwater Quality Prediction Using Proximal Hyperspectral Sensing, GIS, and machine learning algorithms

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WATER AIR AND SOIL POLLUTION 2025年第6期236卷 1-25页

作者： Raheja, Hemant Goel, Arun Pal, Mahesh Natl Inst Technol Kurukshetra Dept Civil Engn Kurukshetra 136119 Haryana India

The primary objective of the present study is to assess the suitability of groundwater for drinking purposes using the Water Quality Index (WQI), Geographic Information System (GIS), and proximal sensing techniques. For this purpose, 272 groundwater samples collected before and after the monsoon period were analyzed for various hydrochemical parameter concentrations. To explore the relationship between water spectral reflectance and WQI, a spectroradiometer was used to measure the reflectance of each sample under laboratory conditions. Four machine learning procedures i.e., Support Vector Regression (SVR), M5P, Random Forest (RF), and Gaussian Process Regression (GPR) were used to predict WQI using resampled spectral reflectance. The WQI analysis revealed that approximately 30% of the groundwater samples fell within the poor to extremely poor-quality category in both periods, while 55.88% (Pre-monsoon) and 55.14% (Post-monsoon) of samples were classified under moderate quality, indicating marginal suitability for drinking purposes. The spectral curves of WQI indicated a lower reflectance for water samples with lower WQI values, whereas higher reflectance values were associated with higher WQI values. Performance evaluation of machine learning models demonstrated that SVR outperformed M5P, RF, and GPR, achieving the highest Correlation Coefficient (CC = 0.9964) and the lowest Root Mean Square Error (RMSE = 7.0313 mg/L) and Mean Absolute Error (MAE = 3.5056 mg/L) in the training phase, while maintaining similar performance in the testing phase (CC = 0.9964, RMSE = 7.6748 mg/L, MAE = 4.0297 mg/L). These findings highlight the potential of integrating spectral reflectance with machine learning models for accurate groundwater quality assessment and spatial mapping.

关键词： Water Quality Index (WQI) Spatial Distribution Spectral Reflectance machine learning algorithms

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