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IEEE OPEN JOURNAL OF INSTRUMENTATION AND MEASUREMENT 2024年 3卷

作者： Raets, Camille El Aisati, Chaimae Rifi, Amir L. De Ridder, Mark Putman, Koen De Mey, Johan Sermeus, Alexandra Barbe, Kurt Vrije Univ Brussel Biostat & Med Informat BISI Res Grp B-1090 Brussels Belgium Univ Ziekenhuis Brussel Dept Radiotherapy B-1090 Brussels Belgium Vrije Univ Brussel Interuniv Ctr Hlth Econ Res Grp B-1090 Brussels Belgium Univ Ziekenhuis Brussel Dept Gastroenterol B-1090 Brussels Belgium

The growing popularity of artificial intelligence (AI) has increased its widespread adoption in medicine. However, the relationship between AI and medical experts' opinions remains elusive. This study investigated the consistency between Random Forest's prediction for rectal cancer regression grades and doctors' opinion based on clinical data. We examined the impact of grading system subjectivity on the algorithm. Analyzing clinical parameters and medical notes from 85 rectal cancer patients, we identified patients with ambivalent grades, the "gray-zone patients," and explored the algorithm's difficulty in predicting their regression grade. We also introduced a regularization parameter to test if some patients could still correctly be predicted when some statistical information is suppressed. Our results demonstrated that the gray-zone patients were significantly more difficult to classify using the algorithm, suggesting that such patients should be reviewed twice to reduce errors. Additionally, we observed that the regularization parameter did not benefit gray-zone patients as much as others. Our findings emphasize the need for AI and clinical experts to work collaboratively since the algorithm cannot consider the subjectivity that medical experts can identify. Further research is necessary to incorporate subjectivity into AI algorithms to enhance their predictive capabilities and further bridge the gap between medicine and AI.

关键词： Artificial intelligence Prediction algorithms Tumors Radiomics Medical diagnostic imaging Classification algorithms Cancer Medical services Feature extraction machine learning algorithms Computed tomography Dworak regression grade machine learning (ML) medical imaging prediction algorithm random forest rectal cancer

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Groundwater potential mapping in arid and semi-arid regions of kurdistan region of Iraq: A geoinformatics-based machine learning approach

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GROUNDWATER FOR SUSTAINABLE DEVELOPMENT 2024年 27卷

作者： Fatah, Kaiwan K. Mustafa, Yaseen T. Hassan, Imaddadin O. Salahaddin Univ Erbil Dept Earth Sci & Petr Erbil Kurdistan Reg Iraq Univ Zakho Dept Environm Sci Zakho Kurdistan Reg Iraq Univ Zakho Ctr Appl Remote Sensing & GIS Zakho Kurdistan Reg Iraq

Groundwater (GW) is a crucial and increasingly scarce natural resource, that is affected by climate change and mismanagement. To manage GW resources effectively, it is crucial to accurately identify GW potential zones (GWPZs) using modern techniques. This study aimed to employ and assess geoinformatics-based machine learning (ML) models to delineate GWPZs in the Akre district, Kurdistan region of Iraq. Six nonparametric ML models were used: a support vector machine (SVM), k-nearest neighbours (KNN), decision tree (DT), random forest (RF), gradient boost DT (GBDT), and extreme gradient boosting (XGBoost). These models were trained on diverse GWPZ-favourable influencing factors, encompassing topographic, hydrological, geological, and environmental aspects. The findings of this study revealed that the XGBoost model outperformed the other nonparametric models in terms of best-fit performance and accuracy in generating a GW potential map (GWPM), achieving a R2 of 0.88, a root mean square error (RMSE) of 11.348, and a mean absolute error (MAE) of 6.623. Notably, over half of the study area (53%) was categorised as having high or very high GWPZs, primarily in the low-lying Rovia Plain. The study identified rainfall, elevation, lineament density (LD), drainage density (Dd), topographic wetness index (TWI) , slope as the most significant factors influencing GWPZ modelling. This study provides a comprehensive framework for GW resource management, ecological conservation, , urban development planning. These insights are crucial for stakeholders, policymakers, and local authorities in stra- tegic resource planning and environmental stewardship.

关键词： Groundwater potential zones machine learning algorithms Geoinformatics XGBoost model Influencing factors Kurdistan region of Iraq

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Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy

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SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES 2024年第12期38卷 7656-7662页

作者： Sunakawa, Taiki Kitaguchi, Daichi Kobayashi, Shin Aoki, Keishiro Kujiraoka, Manabu Sasaki, Kimimasa Azuma, Lena Yamada, Atsushi Kudo, Masashi Sugimoto, Motokazu Hasegawa, Hiro Takeshita, Nobuyoshi Gotohda, Naoto Ito, Masaaki Natl Canc Ctr Hosp East Dept Hepatobiliary & Pancreat Surg 6-5-1 Kashiwanoha Kashiwa Chiba 2778577 Japan Juntendo Univ Grad Sch Med Course Adv Clin Res Canc 2-1-1HongoBunkyo Ku Tokyo 1138421 Japan Natl Canc Ctr Hosp East Surg Device Innovat Off 6-5-1 Kashiwanoha Kashiwa Chiba 2778577 Japan

Background Intraoperative hemorrhage during laparoscopic hepatectomy (LH) is a risk factor for negative postoperative outcomes. Ensuring appropriate hemostasis enhances the safety of surgical procedures. An automatic bleeding recognition system based on deep learning can lead to safer surgeries;however, deep learning models that are useful for detecting and stopping bleeding in LH have not yet been reported. In this study, we aimed to develop a deep learning model to automatically recognize bleeding regions during liver transection in LH. Methods In this retrospective feasibility study, bleeding scenes were randomly selected from LH videos, and the videos were divided into frames at 30 frames per second. Bleeding regions within the images were annotated by pixels, and subsequently, all images were assigned to the training, validation, and test datasets to develop the deep learning model. A convolutional neural network algorithm was used to perform semantic segmentation. After training and validation, the model was evaluated using images from the test dataset. Precision, recall, and Dice coefficients served as the evaluation metrics for the model. Results In total, 2203 annotated images from 44 LH videos were utilized and divided into 1500, 400, and 303 frames for the training, validation, and test datasets, respectively. The precision, recall, and Dice coefficient values of the model were 0.76, 0.79, and 0.77, respectively. Conclusions We developed an automatic bleeding recognition model based on semantic segmentation and verified its performance. The proposed model is potentially useful for intraoperative alerting or evaluating surgical skills in the future.

关键词： machine learning algorithms Real-time bleeding detection Surgical assistance systems Intraoperative hemorrhage monitoring Laparoscopic surgery advancements Surgical safety enhancements

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Classifying arsenic-contaminated waters in Tarkwa: a machine learning approach

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SUSTAINABLE WATER RESOURCES MANAGEMENT 2024年第2期10卷 55页

作者： Ayisha, Mohammed Nkoom, Matthew Doke, Dzigbodi Adzo Univ Dev Studies Fac Nat Resources & Environm Dept Environm & Sustainabil Sci Tamale Ghana Univ Environm & Sustainable Dev Sch Built Environm Somanya Ghana

Access to clean and safe drinking water is key to the improvement of social lives in most developing countries. Due to its hazardous nature and detrimental effects on human health, increased quantities of arsenic in water bodies have been a growing global health concern in recent years. In Ghana, elevated arsenic concentration is reported in some waters in Tarkwa. However, constant monitoring of arsenic concentrations in these water sources are inhibited by the associated huge expenses. To facilitate early detection, this study aimed at developing efficient machine learning models for classifying high, medium and low levels of arsenic contamination using physical water parameters, such as total dissolved solids, pH, electrical conductivity and turbidity. These parameters were selected, because they are relatively inexpensive to measure, their data were available and they may influence the concentration of arsenic in the water. Thus, three machine learning models, namely, extra trees, random forest and decision tree, were developed and assessed using evaluation metrics, such as accuracy, precision and sensitivity. The evaluation results justified the superiority of the extra trees and random forest models over decision tree. However, all developed machine learning models generally gave remarkable performance when classifying waters with high and low levels of arsenic contamination. Moreover, the variable importance analysis revealed that pH had the strongest influence in classifying arsenic contaminated waters followed by electrical conductivity. The outcome of the study has revealed the potency of machine learning algorithms in assisting water monitoring practitioners for monitoring arsenic concentration in water sources.

关键词： Water quality Arsenic machine learning algorithms Extra trees Random forest Decision tree

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Combing chemical composition profiling with machine learning for geographical origins identification of Nardostachys jatamansi DC.

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MICROCHEMICAL JOURNAL 2024年 207卷

作者： Feng, Wanqing Zhou, Lili Han, Ying Zhang, Tingting Wen, Jiawei Chen, Chao Wang, Yan He, Yang Chengdu Univ Tradit Chinese Med Coll Med Technol State Key Lab Southwestern Chinese Med Resources Chengdu 611137 Sichuan Peoples R China China Natl Nucl Corp Chengdu Med Coll Affiliated Hosp 2 Hosp 416 Chengdu 610051 Sichuan Peoples R China Chengdu Med Coll Sch Lab Med Chengdu 610500 Sichuan Peoples R China Analyt & Metr Ctr Sichuan Prov Chengdu 610023 Sichuan Peoples R China

Nardostachys jatamansi DC., a herb endemic to the Himalayas, has a long history of use in ayurvedic and other traditional medicine systems. The quality of N. jatamansi materials is closely related to their geographical origins. This study aims to develop a method for quickly and accurately identifying the geographical origin of N. jatamansi samples. A total of 118 samples of N. jatamansi samples were collected from five production areas in China. High performance liquid chromatography (HPLC), inductively coupled plasma mass spectrometry (ICPMS) and gas chromatography-mass spectrometry (GC-MS) were used to characterize the active compounds, mineral elements, and volatile components in N. jatamansi, and four algorithms were introduced to explore the potential of various machine learning modeling techniques for geographical origin authentication. Three active compounds, 8 mineral elements and 24 volatile metabolites in N. jatamansi were identified to be the key information for origin traceability and authentication. The fused data based on the 3 active compounds and the other feature variables led to the highest accuracies for origin classification, with the accuracies of linear discriminant analysis (LDA), K-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) are 94.44 %, 100 %, 100 %, and 100 %, respectively. This study demonstrates the effectiveness of a multi-source data fusion strategy for geographical origin traceability of N. jatamansi.

关键词： Nardostachys jatamansi DC. Geographical origin traceability HPLC ICP-MS GC-MS machine learning algorithms

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Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2024年第4期6卷

作者： Rout, Smitanjali Samal, Sudhansu Kumar Mahapatro, Soumya Ranjan Centurion Univ Technol & Management Dept Elect Engn Bhubaneswar Orissa India Vellore Inst Technol Chennai Sch Elect Engn Chennai 600127 Tamil Nadu India

The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting real-time data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability

关键词： Li-ion battery BMS coulomb counting UKF SOC machine learning algorithms EV

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Employing machine learning techniques for prediction of micronutrient supplementation status during pregnancy in East African Countries

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Ngusie, Habtamu Setegn Enyew, Ermias Bekele Walle, Agmasie Damtew Assaye, Bayou Tilahun Kasaye, Mulugeta Desalegn Tesfa, Getanew Aschalew Zemariam, Alemu Birara Woldia Univ Coll Med & Hlth Sci Sch Publ Hlth Dept Hlth Informat POB 400 Woldia Amhara Ethiopia Wollo Univ Coll Med & Hlth Sci Dept Hlth Informat Dessie Ethiopia Debre Berhan Univ Coll Med & Hlth Sci Dept Hlth Informat Debre Berhan Ethiopia Debre Markos Univ Coll Hlth Sci Dept Hlth Informat Debre Markos Ethiopia Dilla Univ Sch Publ Hlth Dilla Ethiopia Woldia Univ Coll Med & Hlth Sci Sch Nursing Dept Pediat & Child Hlth Nursing Woldia Ethiopia

Micronutrient deficiencies, known as "hidden hunger" or "hidden malnutrition," pose a significant health risk to pregnant women, particularly in low-income countries like the East Africa region. This study employed eight advanced machine learning algorithms to predict the status of micronutrient supplementation among pregnant women in 12 East African countries, using recent demographic health survey (DHS) data. The analysis involved 138,426 study samples, and algorithm performance was evaluated using accuracy, area under the ROC curve (AUC), specificity, precision, recall, and F1-score. Among the algorithms tested, the random forest classifier emerged as the top performer in predicting micronutrient supplementation status, exhibiting excellent evaluation scores (AUC = 0.892 and accuracy = 94.0%). By analyzing mean SHAP values and performing association rule mining, we gained valuable insights into the importance of different variables and their combined impact, revealing hidden patterns within the data. Key predictors of micronutrient supplementation were the mother's education level, employment status, number of antenatal care (ANC) visits, access to media, number of children, and religion. By harnessing the power of machine learning algorithms, policymakers and healthcare providers can develop targeted strategies to improve the uptake of micronutrient supplementation. Key intervention components involve enhancing education, strengthening ANC services, and implementing comprehensive media campaigns that emphasize the importance of micronutrient supplementation. It is also crucial to consider cultural and religious sensitivities when designing interventions to ensure their effectiveness and acceptance within the specific population. Furthermore, researchers are encouraged to explore and experiment with various techniques to optimize algorithm performance, leading to the identification of the most effective predictors and enhanced accuracy in predicting micronutrient

关键词： Micronutrient supplementation Pregnant women Demographic health survey machine learning algorithms

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Screening for immune-related biomarkers associated with myasthenia gravis and dilated cardiomyopathy based on bioinformatics analysis and machine learning

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HELIYON 2024年第7期10卷 e28446页

作者： Zhou, Guiting Wang, Shushu Lin, Liwen Lu, Kachun Lin, Zhichao Zhang, Ziyan Zhang, Yuling Cheng, Danling Szeto, Kaman Peng, Rui Luo, Chuanjin Guangzhou Univ Chinese Med Clin Med Coll 1 Guangzhou Peoples R China Zhongshan Hosp Tradit Chinese Med Zhongshan Peoples R China Guangzhou Univ Chinese Med Shunde Hosp Foshan Peoples R China Guangzhou Univ Chinese Med Affiliated Hosp 1 Cardiol Ctr Guangzhou Peoples R China Guangdong Clin Res Acad Chinese Med Guangzhou Peoples R China 12 Jinsha Ave Foshan Peoples R China 12 Airport Rd Guangzhou Peoples R China

Background: We aim to investigate genes associated with myasthenia gravis (MG), specifically those potentially implicated in the pathogenesis of dilated cardiomyopathy (DCM). Additionally, we seek to identify potential biomarkers for diagnosing myasthenia gravis co-occurring with DCM. Methods: We obtained two expression profiling datasets related to DCM and MG from the Gene Expression Omnibus (GEO). Subsequently, we conducted differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on these datasets. The genes exhibiting differential expression common to both DCM and MG were employed for protein -protein interaction (PPI), Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Additionally, machine learning techniques were employed to identify potential biomarkers and develop a diagnostic nomogram for predicting MG -associated DCM. Subsequently, the machine learning results underwent validation using an external dataset. Finally, gene set enrichment analysis (GSEA) and machine algorithm analysis were conducted on pivotal model genes to further elucidate their potential mechanisms in MG -associated DCM. Results: In our analysis of both DCM and MG datasets, we identified 2641 critical module genes and 11 differentially expressed genes shared between the two conditions. Enrichment analysis disclosed that these 11 genes primarily pertain to inflammation and immune regulation. Connectivity map (CMAP) analysis pinpointed SB-216763 as a potential drug for DCM treatment. The results from machine learning indicated the substantial diagnostic value of midline 1 interacting protein1 (MID1IP1) and PI3K-interacting protein 1 (PIK3IP1) in MG -associated DCM. These two hub genes were chosen as candidate biomarkers and employed to formulate a diagnostic nomogram with optimal diagnostic performance through machine learning. Simultaneously, single -gene GSEA results and immune cell infiltratio

关键词： Dilated cardiomyopathy Myasthenia gravis Immune cell infiltration Diagnostic value machine learning algorithms

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Analysis of Gait Motion Sensor Mobile Authentication with machine learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第3期15卷 9-16页

作者： Kokal, Sara Vanamala, Mounika Dave, Rushit Univ Wisconsin Eau Claire Comp Sci Dept Eau Claire WI 54701 USA Minnesota State Univ Comp Informat Sci Dept Mankato MN USA

In recent decades, mobile devices have evolved in potential and prevalence significantly while advancements in security have stagnated. As smartphones now hold unprecedented amounts of sensitive data, there is an increasing need to resolve this gap in security. To address this issue, researchers have experimented with biometric-based authentication methods to improve smartphone security. Following a comprehensive review, it was found that gait-based mobile authentication is under-researched compared to other behavioral biometrics. This study aims to contribute to the knowledge of biometric and gait-based authentication through the analysis of recent gait datasets and their potential with machine learning algorithms. Two recently published gait datasets were used with algorithms such as Random Forest, Decision Tree, and XGBoost to successfully differentiate users based on their respective walking features. Throughout this paper, the datasets, methodology, algorithms, experimental results, and goals for future work will be described.

关键词： machine learning machine learning algorithms behavioral biometrics gait dynamics motion sensors

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Optimized Hybrid Convolution Neural Network with machine learning for Arabic Sign Language Recognition

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TRAITEMENT DU SIGNAL 2024年第4期41卷 1835-1846页

作者： Mahmoud, Ahmed Osman Ziedan, Aibrahim Zamel, Amr A. Zagazig Univ Fac Engn Comp & Syst Engn Dept Zagazig 44519 Egypt

The World Health Organization stated that the global population of hard of hearing individuals is estimated to exceed 360 million people, and this number is continuously increasing. Communication barriers between these individuals and hearing individuals pose significant challenges in many areas of life, including education and employment. Therefore, developing methods to facilitate communication and bridge communication gaps is essential. In this paper, a novel approach is presented to Arabic sign alphabets recognition using optimized hybrid techniques. The proposed approach combines a convolutional neural network with five traditional machine learning algorithms: Feedforword Neural network, Decision Tree, Random Forest, Support Vector machine and K-Nearest Neighbors. The proposed approach was evaluated on a large dataset called ArSL2018, Awhich consists of 32 different classes of Arabic alphabet letters. ASix different optimization techniques were investigated to find the optimal multipliers for the outputs of the Hybrid CNN models to achieve high classification accuracy. These techniques included the Genetic Algorithm, Particle Swarm, Firefly Algorithm, Differential Evolution, Sine Cosine, and Harris Hawks Optimization. The experiments demonstrated that the optimized hybrid techniques achieved the highest accuracy, approaching 99%, surpassing their counterparts. This demonstrates the efficiency of the proposed model inAaccurately recognizing Arabic alphabets.

关键词： classification convolution neural network hybrid CNN hybrid ML machine learning algorithms optimization sign language

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