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Optimization of graphene-based biosensor design for haemoglobin detection using the gradient boosting algorithm for behaviour prediction

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MEASUREMENT 2025年 239卷

作者： Wekalao, Jacob Srinivasan, Guru Prasad Patel, Shobhit K. Al-zahrani, Fahad Ahmed Natl Forens Sci Univ Dept Engn & Technol Gandhinagar 382007 Gujarat India Saveetha Univ Saveetha Med Coll & Hosp Saveetha Inst Med & Tech Sci Ctr Global Hlth Res Chennai 602105 Tamilnadu India Marwadi Univ Dept Comp Engn Rajkot 360003 India Umm Al Qura Univ Comp Engn Dept Mecca 24381 Saudi Arabia

This study presents an advanced biosensor based on metasurfaces for hemoglobin detection. The proposed sensor design integrates graphene with gold and silver, leveraging their exceptional optical properties and ability to support surface plasmon resonances. The metasurface-based architecture enhances interactions between the sensor and hemoglobin biomolecules, resulting in improved sensitivity and other performance parameters. Extensive optimization of the design parameters, including resonator dimensions and graphene chemical potential, was conducted to achieve an optimized sensor design. The sensor exhibits exceptional characteristics, including a peak sensitivity of 267 GHzRIU- 1, a quality factor of 10.457, and a sensor resolution of 0.094, among other remarkable performance metrics. To streamline the optimization process and reduce computational complexity, the gradient boosting algorithm (GBoost) is integrated into this study for behaviour prediction. The GBoost model demonstrates impressive performance, including an optimal coefficient of determination (R2) score of 1.0 for all cases considered, indicating perfect predictive accuracy within the model's scope. These outstanding results suggest the significant potential of the proposed biosensor for rapid and precise blood testing, as well as monitoring medical conditions such as anaemia, by enabling early and accurate detection of hemoglobin levels. The sensor's high-performance metrics, coupled with its simple design, represent a substantial advancement in the field of biosensing technology.

关键词： Metamaterials Metasurfaces biosensor Graphene Plasmonic gradient boosting algorithm Refractive index sensing Terahertz spectroscopy

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Research on Accurate Prediction Method of Epilepsy Stage Based on gradient boosting algorithm 12

Research on Accurate Prediction Method of Epilepsy Stage Bas...

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12th International Conference on Information Systems and Computing Technology

作者： Liu, Yuetong Sun, Hongyu Ge, Yuxin Dong, Yanhua Song, Xinyu Jilin Normal Univ Coll Math & Comp Siping Peoples R China

ISBN: (纸本)9798350379860;9798350379877

With the incidence rate of epilepsy increasing year by year, early diagnosis and accurate prediction of epilepsy have become a significant Research Interests in the medical field. This article explores a method of forecasting for the onset stage of epilepsy based on gradient boosting algorithm, aiming to improve the accurate prediction rate of epilepsy patients during the onset period. The prediction is based on a large amount of clinical data and EEG data of epilepsy patients, which have been standardized to improve model performance and accelerate model training speed. Train the preprocessed data using gradient boosting algorithm to reveal the correlation between learning features and the onset stage of epilepsy. The gradient boosting algorithm has unique flexibility and can cope with diverse data features. Through mechanisms such as fine feature selection, prevention of overfitting, and missing value processing, it achieves high accuracy and strong generalization ability, providing an elegant and powerful solution for tasks such as data mining and predictive modeling in various fields. Compared to traditional diagnostic methods, the prediction method based on gradient boosting algorithm has significantly improved accuracy, fully verifying the potential and advantages of gradient boosting algorithm in predicting the onset stage of epilepsy. This article verifies the accuracy and stability of gradient boosting algorithm in predicting the incidence of epilepsy through indicator evaluation, which can provide more accurate diagnostic basis for medical personnel, thereby improving the treatment effect and living quality of epilepsy patients.

关键词： Epilepsy Patients EEG Signals gradient boosting algorithm

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gradient boosting algorithm for current-voltage prediction of fuel cells

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ELECTROCHIMICA ACTA 2022年 432卷

作者： Park, Jihyeon Lee, Jaeyoung Gwangju Inst Sci & Technol Sch Earth Sci & Environm Engn 123 Cheomdangwagi ro Gwangju 61005 South Korea Gwangju Inst Sci & Technol Int Future Res Ctr Chem Energy Storage & Convers P 123 Cheomdangwagi ro Gwangju 61005 South Korea Gwangju Inst Sci & Technol Ertl Ctr Electrochem & Catalysis 123 Cheomdangwagi ro Gwangju 61005 South Korea

The developed catalyst performs well in a half-cell test, but since the catalyst performance is not completely shown in a single-cell test, single-cell optimization through repeated experiments is essential. This is because single-cell is a complex process system affected by various factors, not just a catalyst evaluation system. To reduce the number of laborious tests, we used a machine learning algorithm to prioritize an alkaline fuel cell's operational factor from types of catalyst to fuel concentration for developing an overall performance prediction model. We selected seventeen input features from more than 80 I-V curves and 8000 data points and established prediction models based on two error-scoring modes (mean absolute error and root mean square error), which focused on operational conditions rather than catalytic characteristics. Both models from the two modes charted the factor importance and predicted the overall fuel cell performance with high R-2 values (> 0.95) before experiments.

关键词： Performance prediction Alkaline fuel cell Machine learning Artificial intelligence gradient boosting algorithm

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Kannada Dialect Identification from Case-Based Word Utterances Using gradient boosting algorithm 1st

Kannada Dialect Identification from Case-Based Word Utteranc...

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1st International Conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Chittaragi, Nagaratna B. Koolagudi, Shashidhar G. Siddaganga Inst Technol Dept ISE Tumakuru Karnataka India Natl Inst Technol Karnataka Dept CSE Surathkal India

ISBN: (纸本)9783030960407;9783030960391

Dialects or accents constitute the grammatical variations along with phonological and lexical changes those are commonly observed in the usage of a language with minor and subtle differences. Dialectal variations existing among dialects are mainly due to unique speaking patterns followed among the group of speakers. The dialect processing systems are essential in the development of automatic speech recognition systems (ASRs) for regional and resource-constrained languages in the country like India. Since India is with rich diversity in languages. In this paper, a language-dependent dialect identification system is proposed for Kannada language from words especially with the Kannada language-specific case (Vibhakthi Prathyayas) information. Special morphological operations that exist in the Kannada language in terms of various cases commonly called as a grammatical function of a noun or pronoun. These word utterances are used for the classification of five dialects of Kannada. This is a novel idea to use the smaller word utterances that consist of dialect-specific information representing the unique characteristics. In this paper, case-based word utterance dataset is prepared by considering five Kannada dialects from Kannada Dialect Speech Corpus (KDSC). Dynamic and static prosodic features are extracted to capture dialectal variations. Addition to these features, spectral MFCC features are also considered for evaluation of differences among dialects from these word-level units. Initially, multi-class Support vector machine (SVM) technique is used and later effective extreme gradient boosting (XGB) ensemble algorithms are used for the development of an automatic Kannada dialect recognition system. The research findings have demonstrated the words with case information convey dialect specific linguistic cues effectively. The combination of dynamic and static prosodic cues has a significant effect on the characterization of dialects along with spectral features.

关键词： Kannada dialect identification Cases (Vibhakthi Prathyayas) Dynamic and static prosodic features gradient boosting algorithm Support vector machines

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Online comment-based prediction of cosmetic ingredient's sensory irritation using gradient boosting algorithm

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JOURNAL OF COSMETIC DERMATOLOGY 2020年第7期19卷 1676-1683页

作者： Jiang, Biao Wang, Huijuan Cheng, Li Zi, Yusha He, Congfen Den, Yi-Ann Beijing Technol & Business Univ Sch Sci Beijing Key Lab Plant Resources Res & Dev Beijing Peoples R China Wellness GPS Pte Ltd Singapore Singapore Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

Background The worldwide prevalence of "sensitive skin" group is estimated at being close to 40%. The main trigger for sensitive skin is the misuse of cosmetics products. Majority of the in vitro studies on cosmetic ingredients developed for topical application to the skin are not able to describe sensory irritation, such as stinging, burning, itching, and tingling. Besides, most of the in vivo tests often encounter problems such as limited number of subjects and usage scenarios deviate from reality. Objective A gradient boosting algorithm is adopted in our context to integrate multisource of information including skin types, sensory response, and cosmetics ingredients to predict sensory irritation. Method In this study, online comments were preprocessed to construct a multi-dimensional structured data including skin types, sensory response, and cosmetics ingredients. A gradient boosting regression model was developed where sensory response was predicted based on the abovementioned structured input. The predictions were validated by in vivo test and were shown favorably when comparing with the state-of-the-art results from related works. Result 46 007 samples were collected after data cleaning and were used in model developing. Results showed that the model was capable to predict the sensory response of 16 skin types to different ingredients (R = 0.71, P < 10(-10)). In addition, this model was validated using data from in vivo studies and presented a value of 75% on specificity, 88.9% on sensitivity, and 82.4% on accuracy. Conclusion Our approach that is based on a variant of the gradient boosting algorithm provided an adequate solution for understanding the sensory irritation of cosmetic ingredients.

关键词： gradient boosting algorithm online comment sensitive skin sensory irritation

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The Prediction of Day-to-Day Occurrence of Low Latitude Ionospheric Strong Scintillation Using gradient boosting algorithm

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SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS 2021年第12期19卷 e2021SW002884-e2021SW002884页

作者： Zhao, Xiukuan Li, Guozhu Xie, Haiyong Hu, Lianhuan Sun, Wenjie Yang, Sipeng Li, Yi Ning, Baiqi Takahashi, Hisao Chinese Acad Sci Mohe Observ Geophys Inst Geol & Geophys Beijing Peoples R China Natl Earth Syst Sci Data Ctr Geophys Ctr Beijing Peoples R China Chinese Acad Sci Key Lab Earth & Planetary Phys Inst Geol & Geophys Beijing Peoples R China Chinese Acad Sci Beijing Natl Observ Space Environm Inst Geol & Geophys Beijing Peoples R China Univ Chinese Acad Sci Coll Earth & Planetary Sci Beijing Peoples R China Chinese Acad Sci Innovat Acad Earth Sci Beijing Peoples R China Inst Nacl Pesquisas Espaciais Div Aeron Sao Jose Dos Campos Brazil

Ionospheric scintillations caused by equatorial plasma bubbles (EPBs) can seriously affect various high technology systems based on Global Navigation Satellite System (GNSS) signals at equatorial and low latitudes. A reliable prediction of ionospheric scintillation occurrence is critical to relieve the effect. Using the long-term ground-based GNSS receiver and ionosonde data collected in the Brazilian longitude sector during 2012-2020, an ionospheric strong scintillation prediction model based on the gradient boosting algorithms extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and CatBoost is created and tested. It is for the first time that the XGBoost, LightGBM, and CatBoost are utilized to predict the day-to-day occurrence of regional ionospheric scintillation during post-sunset hours. The relative importance of different parameters affecting EPB/scintillation occurrence for building the prediction model is examined. A comparison of daily scintillation occurrence from the modeled and observed results during 2014 (solar maximum) and 2020 (solar minimum) shows that the gradient boosting algorithms are effective for predicting strong scintillations over low latitude, with a prediction accuracy of similar to 85%. The results suggest that the trained model with input of total electron content, equatorial F layer peak height and critical frequency before sunset could be well employed to predict the occurrence/nonoccurrence of intense scintillations over low latitude after sunset on a daily basis.

关键词： ionospheric scintillation prediction gradient boosting algorithm Equatorial F layer height

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Decision based uncertainty model to predict rockburst in underground engineering structures using gradient boosting algorithms

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GEOMECHANICS AND ENGINEERING 2022年第3期30卷 259-272页

作者： Kidega, Richard Ondiaka, Mary Nelima Maina, Duncan Jonah, Kiptanui Arap Too Kamran, Muhammad Taita Taveta Univ Sch Mines & Engn Dept Min & Mineral Proc Engn POB 63580300 Voi Kenya Inst Technol Bandung Dept Min Engn Bandung Indonesia

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.

关键词： accuracy gradient boosting algorithm modelling rockburst sensitivity specificity

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Time series-based prediction of antibiotic degradation via photocatalysis using ensemble gradient boosting

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2024年第16期31卷 24315页

作者： Sethi, Sheetal Dhir, Amit Arora, Vinay Thapar Inst Engn & Technol Sch Energy & Environm Patiala India Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala India

This study aims to evaluate the effectiveness of the laboratory-made catalyst Ni2P-ZrO2 (NPZ) in the degradation of an antibiotic in an aqueous suspension when exposed to ultraviolet (UV) light. The degradation of amoxicillin (AMX) was predicted using time series forecasting through the ensemble gradient boosting model. The degradation experiments were conducted utilizing two distinct photocatalyst compositions of Nickel phosphide-zirconium dioxide (NPZ) in the proportions of 1:9 and 2:8. The most effective experimental results were obtained using a natural pH, a catalyst concentration of 0.20 g/L and reaction duration of 0.5 h after testing the different catalysts. Experimental data were used for training, validating and confirming time series predictions. The use of ensemble technique highly affected the experimental findings. The model's performance was quite satisfactory in terms of correlation coefficient (94.00%), normalized mean square error (0.01) and mean square root error (0.0911) which significantly contributed to the model's accuracy. All input variables, such as pH, catalyst dose and irradiation time, had a significant impact on the degrading efficacy. The study has demonstrated that time series forecasting can be used for predicting the degradation process precisely.

关键词： Photocatalysis gradient boosting algorithm Time series prediction Antibiotic degradation Catalysis

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A Comprehensive framework for Parkinson's disease diagnosis using explainable artificial intelligence empowered machine learning techniques

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ALEXANDRIA ENGINEERING JOURNAL 2024年 107卷 568-582页

作者： Priyadharshini, S. Ramkumar, K. Vairavasundaram, Subramaniyaswamy Narasimhan, K. Venkatesh, S. Amirtharajan, Rengarajan Kotecha, Ketan SASTRA Deemed Univ Sch Elect & Elect Engn Thanjavur India SASTRA Deemed Univ Sch Comp Thanjavur India Vellore Inst Technol Sch Comp Sci & Engn Vellore India Symbiosis Int Symbiosis Inst Technol Symbiosis Ctr Appl Artificial Intelligence Pune India

Parkinson's disease (PD) is the second most prevalent neurological disorder, predominantly affecting older people. With no existing cure, the early detection of PD, where symptoms are not entirely evident but indicative of the disease's onset, is critical. This study aims to design and develop AI-based diagnostic methods that can detect these early signs of PD with high accuracy, thereby facilitating more effective disease management. This study focuses on developing a method that not only identifies PD at an early stage but also provides clinicians with a tool to interpret the decisions taken by the AI models to avoid misdiagnosis. In this study, a T2-weighted 3D Magnetic resonance imaging (MRI) dataset is used to analyze detailed morphological, textural, and structural changes. The MRI scans are pre-processed using brain extraction, image registration, bias correction, normalization, and segmentation processes. Upon segmentation, feature extraction was applied to the segmented subcortical regions using radiomics tools, resulting in the extraction of 107 features. The top 20 features were selected through Pearson's correlation, recursive feature elimination, and a ranking model, which are responsible for the ML model's class prediction. Statistical validation of these features was also performed using Analysis of Variance (ANOVA), pairwise t-tests, and Kruskal-Wallis H-tests to ensure that the identified 20 features were dominant for the prediction. Based on the identified features, several Machine Learning (ML) models were used to identify the best classifier for the provided real-world MRI scans. The gradient boosting (GB) algorithm achieved better prediction accuracy among the compared models. Incorporating the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalances significantly improved the model's performance, boosting accuracy to 96.8 % from 87 %. Further, multiple Explainable Artificial Intelligence (XAI) techniques were deployed to enh

关键词： Parkinson's disease gradient boosting algorithm Magnetic resonance imaging analysis Explainable artificial intelligence Early detection

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Noise Figure Estimation of EDFA Based on gradient boosting Regression Approach for THz Applications

Noise Figure Estimation of EDFA Based on Gradient Boosting R...

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Workshop on Microwave Theory and Techniques in Wireless Communications (MTTW)

作者： Sadik, Serif Ali Kutahya Dumlupinar Univ Photon Technol Applicat & Res Ctr Kutahya Turkey

ISBN: (数字)9781665464390

ISBN: (纸本)9781665464390

Optical communication systems operating in the THz region require monitoring and control of transmission quality for higher network performance. Erbium-doped fiber amplifiers (EDFA) are one of the most important elements of such systems and input power and wavelength depended gain and noise characteristics of EDFAs complicate the network control. In this work, noise figure (NF) parameter of an EDFA was estimated with gradient boosting regressor model. The training and test data for the model were collected experimentally. The predicted values and real values of NF were fitted well with a coefficient of determination value of 0.9742, mean absolute error of 03428, and the root mean square error of 0.4429.

关键词： erbium-doped fiber amplifier noise figure machine learning gradient boosting algorithm

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