Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML...Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML)has emerged as a powerful data analysis tool,widely applied in the prediction,diagnosis,and mechanistic study of kidney transplant rejection.This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection,covering areas such as the construction of predictive models,identification of biomarkers,analysis of pathological images,assessment of immune cell infiltration,and formulation of personalized treatment strategies.By integrating multi-omics data and clinical information,ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation,driving the development of precision medicine in the field of kidney transplantation.Furthermore,this article discusses the challenges faced in existing research and potential future directions,providing a theoretical basis and technical references for related studies.展开更多
Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the ...Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
In this paper,various types of sinusoidal-fed electrical machines,i.e.induction machines(IMs),permanent magnet(PM)machines,synchronous reluctance machines,variable flux machines,wound field machines,are comprehensivel...In this paper,various types of sinusoidal-fed electrical machines,i.e.induction machines(IMs),permanent magnet(PM)machines,synchronous reluctance machines,variable flux machines,wound field machines,are comprehensively reviewed in terms of basic features,merits and demerits,and compared for HEV/EV traction applications.Their latest developments are highlighted while their electromagnetic performance are quantitatively compared based on the same specification as the Prius 2010 interior PM(IPM)machine,including the torque/power-speed characteristics,power factor,efficiency map,and drive cycle based overall efficiency.It is found that PM-assisted synchronous reluctance machines are the most promising alternatives to IPM machines with lower cost and potentially higher overall efficiency.Although IMs are cheaper and have better overload capability,they exhibit lower efficiency and power factor.Other electrical machines,such as synchronous reluctance machines,wound field machines,as well as many other newly developed machines,are currently less attractive due to lower torque density and efficiency.展开更多
Permanent-magnet(PM)machines are the important driving components of various mechanical equipment and industrial applications,such as robot joints,aerospace equipment,electric vehicles,actuators,wind generators and el...Permanent-magnet(PM)machines are the important driving components of various mechanical equipment and industrial applications,such as robot joints,aerospace equipment,electric vehicles,actuators,wind generators and electric traction systems.The PM machines are usually expected to have high torque/power density,low torque ripple,reduced rotor mass,a large constant power speed range or strong anti-magnetization capability to match different requirements of industrial applications.The structural topology of the electric machines,including stator/rotor arrangements and magnet patterns of rotor,is one major concern to improve their electromagnetic performance.However,systematic reviews of structural topology are seldom found in literature.Therefore,the objective of this paper is to summarize the stator/rotor arrangements and magnet patterns of the permanent-magnet brushless machines,in depth.Specifically,the stator/rotor arrangements of the PM machines including radial-flux,axialflux and emerging hybrid axial-radial flux configurations are presented,and pros and cons of these topologies are discussed regarding their electromagnetic performance.The magnet patterns including various surface-mounted and interior magnet patterns,such as parallel magnetization pole pattern,Halbach arrays,spoke-type designs and their variants are summarized,and the characteristics of those magnet patterns in terms of flux-focusing effect,magnetic self-shielding effect,torque ripple,reluctance torque,magnet utilization ratio,and anti-demagnetization capability are compared.This paper can provide guidance and suggestion for the structure selection and design of PM brushless machines for high-performance industrial applications.展开更多
Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing ...Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.展开更多
LEADSFON(XIAMEN)TEXTILE TECH CO.,LTD.is a manufacturer of knitting circular machines.Since 2002,the company has served as an ODM and supporting partner for the Italian brand"PILOTELLI".In 2014,LEADSFON offic...LEADSFON(XIAMEN)TEXTILE TECH CO.,LTD.is a manufacturer of knitting circular machines.Since 2002,the company has served as an ODM and supporting partner for the Italian brand"PILOTELLI".In 2014,LEADSFON officially acquired PILOTELLI,integrating advanced Italian technology into its core operations.展开更多
During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this...During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.展开更多
The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limite...The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.展开更多
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al...In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.展开更多
The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries.In order to improve the precision and openness of en...The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries.In order to improve the precision and openness of energy consumption projections,this study investigates the combination of machine learning(ML)methods with Shapley additive explanations(SHAP)values.The study evaluates three distinct models:the first is a Linear Regressor,the second is a Support Vector Regressor,and the third is a Decision Tree Regressor,which was scaled up to a Random Forest Regressor/Additions made were the third one which was Regressor which was extended to a Random Forest Regressor.These models were deployed with the use of Shareable,Plot-interpretable Explainable Artificial Intelligence techniques,to improve trust in the AI.The findings suggest that our developedmodels are superior to the conventional models discussed in prior studies;with high Mean Absolute Error(MAE)and Root Mean Squared Error(RMSE)values being close to perfection.In detail,the Random Forest Regressor shows the MAE of 0.001 for predicting the house prices whereas the SVR gives 0.21 of MAE and 0.24 RMSE.Such outcomes reflect the possibility of optimizing the use of the promoted advanced AI models with the use of Explainable AI for more accurate prediction of energy consumption and at the same time for the models’decision-making procedures’explanation.In addition to increasing prediction accuracy,this strategy gives stakeholders comprehensible insights,which facilitates improved decision-making and fosters confidence in AI-powered energy solutions.The outcomes show how well ML and SHAP work together to enhance prediction performance and guarantee transparency in energy usage projections.展开更多
BACKGROUND Delayed wound healing is a common clinical complication following gastric cancer radical surgery,adversely affecting patient prognosis.With advances in artificial intelligence,machine learning offers a prom...BACKGROUND Delayed wound healing is a common clinical complication following gastric cancer radical surgery,adversely affecting patient prognosis.With advances in artificial intelligence,machine learning offers a promising approach for developing predictive models that can identify high-risk patients and support early clinical intervention.AIM To construct machine learning-based risk prediction models for delayed wound healing after gastric cancer surgery to support clinical decision-making.METHODS We reviewed a total of 514 patients who underwent gastric cancer radical surgery under general anesthesia from January 1,2014 to December 30,2023.Seventy percent of the dataset was selected as the training set and 30%as the validation set.Decision trees,support vector machines,and logistic regression were used to construct a risk prediction model.The performance of the model was evaluated using accuracy,recall,precision,F1 index,and area under the receiver operating characteristic curve and decision curve.RESULTS This study included five variables:Sex,elderly,duration of abdominal drainage,preoperative white blood cell(WBC)count,and absolute value of neutrophils.These variables were selected based on their clinical relevance and statistical significance in predicting delayed wound healing.The results showed that the decision tree model outperformed the logistic regression and support vector machine models in both the training and validation sets.Specifically,the decision tree model achieved higher accuracy,F1 index,recall,and area under the curve(AUC)values.The support vector machine model also demonstrated better performance than logistic regression,with higher accuracy,recall,and F1 index,but a slightly lower AUC.The key variables of sex,elderly,duration of abdominal drainage,preoperative WBC count,and absolute value of neutrophils were found to be strong predictors of delayed wound healing.Patients with longer duration of abdominal drainage had a significantly higher risk of delayed wound healing,with a risk ratio of 1.579 compared to those with shorter duration of abdominal drainage.Similarly,preoperative WBC count,sex,elderly,and absolute value of neutrophils were associated with a higher risk of delayed wound healing,highlighting the importance of these variables in the model.CONCLUSION The model is able to identify high-risk patients based on sex,elderly,duration of abdominal drainage,preoperative WBC count,and absolute value of neutrophils can provide valuable insights for clinical decision-making.展开更多
BACKGROUND Liver transplantation(LT)is the only curative,life-saving option for children and adults with end-stage liver disease.Due to the well-known shortage and heterogeneity of grafts,split LT(SLT)is an attractive...BACKGROUND Liver transplantation(LT)is the only curative,life-saving option for children and adults with end-stage liver disease.Due to the well-known shortage and heterogeneity of grafts,split LT(SLT)is an attractive strategy to expand the donor pool and reduce waitlist times.Given increased risk of cold ischemia time with SLT,machine perfusion represents a promising option to reduce it and optimize transplant logistics and outcomes.The present communication describes various possible combinations of procurement steps to perform SLT facilitated by placing one or both grafts on a normothermic machine perfusion(NMP)closed circuit device.CASE SUMMARY A 19-month-old female with biliary atresia after failed Kasai portoenterostomy and a 42-year-old woman with unresectable intrahepatic cholangiocarcinoma were selected as recipients for a SLT from a 17-year-old male donor.The SLT generated a left lateral segment and a right trisectional graft of appropriate volume for both recipients.After a mixed in-situ and ex-situ split,in order to improve logistics,the right trisectional graft was placed on a closed circuit NMP device,following an appropriate vascular reconstruction.Both grafts were implanted with excellent short-term outcomes.CONCLUSION Use of NMP with SLT for preservation prior to implantation allows not only for graft optimization but also for significant improvement of transplant logistics.We propose various models and standardization of logistic options for combining SLT with NMP to optimize graft availability and outcomes.展开更多
BACKGROUND Severe dengue children with critical complications have been attributed to high mortality rates,varying from approximately 1%to over 20%.To date,there is a lack of data on machine-learning-based algorithms ...BACKGROUND Severe dengue children with critical complications have been attributed to high mortality rates,varying from approximately 1%to over 20%.To date,there is a lack of data on machine-learning-based algorithms for predicting the risk of inhospital mortality in children with dengue shock syndrome(DSS).AIM To develop machine-learning models to estimate the risk of death in hospitalized children with DSS.METHODS This single-center retrospective study was conducted at tertiary Children’s Hospital No.2 in Viet Nam,between 2013 and 2022.The primary outcome was the in-hospital mortality rate in children with DSS admitted to the pediatric intensive care unit(PICU).Nine significant features were predetermined for further analysis using machine learning models.An oversampling method was used to enhance the model performance.Supervised models,including logistic regression,Naïve Bayes,Random Forest(RF),K-nearest neighbors,Decision Tree and Extreme Gradient Boosting(XGBoost),were employed to develop predictive models.The Shapley Additive Explanation was used to determine the degree of contribution of the features.RESULTS In total,1278 PICU-admitted children with complete data were included in the analysis.The median patient age was 8.1 years(interquartile range:5.4-10.7).Thirty-nine patients(3%)died.The RF and XGboost models demonstrated the highest performance.The Shapley Addictive Explanations model revealed that the most important predictive features included younger age,female patients,presence of underlying diseases,severe transaminitis,severe bleeding,low platelet counts requiring platelet transfusion,elevated levels of international normalized ratio,blood lactate and serum creatinine,large volume of resuscitation fluid and a high vasoactive inotropic score(>30).CONCLUSION We developed robust machine learning-based models to estimate the risk of death in hospitalized children with DSS.The study findings are applicable to the design of management schemes to enhance survival outcomes of patients with DSS.展开更多
The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods...The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.展开更多
The prediction of tool wear in CNC machine tools is a critical aspect of ensuring the efficient operation and longevity of manufacturing equipment.Tool wear significantly impacts machining accuracy,surface finish qual...The prediction of tool wear in CNC machine tools is a critical aspect of ensuring the efficient operation and longevity of manufacturing equipment.Tool wear significantly impacts machining accuracy,surface finish quality,and operational downtime,making its prediction essential for proactive maintenance strategies.This paper explores the integration of Digital Twin technology with tool wear prediction models to enhance the precision and reliability of wear forecasting in CNC machines.We review existing methodologies for tool wear prediction,including physics-based models,data-driven approaches,and hybrid models,with an emphasis on their strengths and limitations.Furthermore,the paper highlights the role of Digital Twin technology in creating real-time,virtual replicas of CNC machines that can dynamically monitor tool wear and provide actionable insights for optimization.By leveraging real-time data and advanced simulation techniques,Digital Twin-based prediction models offer significant improvements over traditional methods.The paper concludes by discussing future directions for integrating machine learning,deep learning,and real-time data analytics into the tool wear prediction process,ultimately contributing to the development of more intelligent and adaptive manufacturing systems.展开更多
Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in ...Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in materials science,traditional approaches often encounter significant challenges related to computational efficiency and scalability,particularly when applied to complex systems.Recent advances in machine learning(ML)have shown tremendous promise in addressing these limitations,enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions.This review provides a concise overview of recent progress in ML-assisted CSP methodologies,with a particular focus on machine learning potentials and generative models.By critically analyzing these advances,we highlight the transformative impact of ML in accelerating materials discovery,enhancing computational efficiency,and broadening the applicability of CSP.Additionally,we discuss emerging opportunities and challenges in this rapidly evolving field.展开更多
Dementia is a neurological disorder that affects the brain and its functioning,and women experience its effects more than men do.Preventive care often requires non-invasive and rapid tests,yet conventional diagnostic ...Dementia is a neurological disorder that affects the brain and its functioning,and women experience its effects more than men do.Preventive care often requires non-invasive and rapid tests,yet conventional diagnostic techniques are time-consuming and invasive.One of the most effective ways to diagnose dementia is by analyzing a patient’s speech,which is cheap and does not require surgery.This research aims to determine the effectiveness of deep learning(DL)and machine learning(ML)structures in diagnosing dementia based on women’s speech patterns.The study analyzes data drawn from the Pitt Corpus,which contains 298 dementia files and 238 control files from the Dementia Bank database.Deep learning models and SVM classifiers were used to analyze the available audio samples in the dataset.Our methodology used two methods:a DL-ML model and a single DL model for the classification of diabetics and a single DL model.The deep learning model achieved an astronomic level of accuracy of 99.99%with an F1 score of 0.9998,Precision of 0.9997,and recall of 0.9998.The proposed DL-ML fusion model was equally impressive,with an accuracy of 99.99%,F1 score of 0.9995,Precision of 0.9998,and recall of 0.9997.Also,the study reveals how to apply deep learning and machine learning models for dementia detection from speech with high accuracy and low computational complexity.This research work,therefore,concludes by showing the possibility of using speech-based dementia detection as a possibly helpful early diagnosis mode.For even further enhanced model performance and better generalization,future studies may explore real-time applications and the inclusion of other components of speech.展开更多
Maintaining high groundwater level(GWL)is important for preventing fires in peatlands.This study proposes GWL prediction using machine learning methods for forest plantations in Indonesian tropical peatlands.Deep neur...Maintaining high groundwater level(GWL)is important for preventing fires in peatlands.This study proposes GWL prediction using machine learning methods for forest plantations in Indonesian tropical peatlands.Deep neural networks(DNN)have been used for prediction;however,they have not been applied to groundwater prediction in Indonesian peatlands.Tropical peatland is characterized by high permeability and forest plantations are surrounded by several canals.By predicting daily differences in GWL,the GWL can be predicted with high accuracy.DNNs,random forests,support vector regression,and XGBoost were compared,all of which indicated similar errors.The SHAP value revealed that the precipitation falling on the hill rapidly seeps into the soil and flows into the canals,which agrees with the fact that the soil has high permeability.These findings can potentially be used to alleviate and manage future fires in peatlands.展开更多
文摘Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs.With the rapid advancement of artificial intelligence technologies,machine learning(ML)has emerged as a powerful data analysis tool,widely applied in the prediction,diagnosis,and mechanistic study of kidney transplant rejection.This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection,covering areas such as the construction of predictive models,identification of biomarkers,analysis of pathological images,assessment of immune cell infiltration,and formulation of personalized treatment strategies.By integrating multi-omics data and clinical information,ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation,driving the development of precision medicine in the field of kidney transplantation.Furthermore,this article discusses the challenges faced in existing research and potential future directions,providing a theoretical basis and technical references for related studies.
文摘Delayed wound healing following radical gastrectomy remains an important yet underappreciated complication that prolongs hospitalization,increases costs,and undermines patient recovery.In An et al’s recent study,the authors present a machine learning-based risk prediction approach using routinely available clinical and laboratory parameters.Among the evaluated algorithms,a decision tree model demonstrated excellent discrimination,achieving an area under the curve of 0.951 in the validation set and notably identifying all true cases of delayed wound healing at the Youden index threshold.The inclusion of variables such as drainage duration,preoperative white blood cell and neutrophil counts,alongside age and sex,highlights the pragmatic appeal of the model for early postoperative monitoring.Nevertheless,several aspects warrant critical reflection,including the reliance on a postoperative variable(drainage duration),internal validation only,and certain reporting inconsistencies.This letter underscores both the promise and the limitations of adopting interpretable machine learning models in perioperative care.We advocate for transparent reporting,external validation,and careful consideration of clinically actionable timepoints before integration into practice.Ultimately,this work represents a valuable step toward precision risk stratification in gastric cancer surgery,and sets the stage for multicenter,prospective evaluations.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
基金This work is partially supported by Guangdong Welling Motor Manufacturing Co.,Ltd and Guangdong Innovative Research Team Program(No.2011N084)China,Valeo Electrical Systems,France,and the Royal Academy of Engineering/Siemens Research Chair Program,UK.
文摘In this paper,various types of sinusoidal-fed electrical machines,i.e.induction machines(IMs),permanent magnet(PM)machines,synchronous reluctance machines,variable flux machines,wound field machines,are comprehensively reviewed in terms of basic features,merits and demerits,and compared for HEV/EV traction applications.Their latest developments are highlighted while their electromagnetic performance are quantitatively compared based on the same specification as the Prius 2010 interior PM(IPM)machine,including the torque/power-speed characteristics,power factor,efficiency map,and drive cycle based overall efficiency.It is found that PM-assisted synchronous reluctance machines are the most promising alternatives to IPM machines with lower cost and potentially higher overall efficiency.Although IMs are cheaper and have better overload capability,they exhibit lower efficiency and power factor.Other electrical machines,such as synchronous reluctance machines,wound field machines,as well as many other newly developed machines,are currently less attractive due to lower torque density and efficiency.
基金Supported by National Natural Science Foundation of China(NSFC)(Grant No.52130505)Zhejiang Provincial Natural Science Foundation of China(Grant No.LD24E050005)+1 种基金Ningbo Key Scientific and Technological Project of China(Grant No.2022Z040)Academic Excellence Foundation of BUAA for PhD Students.
文摘Permanent-magnet(PM)machines are the important driving components of various mechanical equipment and industrial applications,such as robot joints,aerospace equipment,electric vehicles,actuators,wind generators and electric traction systems.The PM machines are usually expected to have high torque/power density,low torque ripple,reduced rotor mass,a large constant power speed range or strong anti-magnetization capability to match different requirements of industrial applications.The structural topology of the electric machines,including stator/rotor arrangements and magnet patterns of rotor,is one major concern to improve their electromagnetic performance.However,systematic reviews of structural topology are seldom found in literature.Therefore,the objective of this paper is to summarize the stator/rotor arrangements and magnet patterns of the permanent-magnet brushless machines,in depth.Specifically,the stator/rotor arrangements of the PM machines including radial-flux,axialflux and emerging hybrid axial-radial flux configurations are presented,and pros and cons of these topologies are discussed regarding their electromagnetic performance.The magnet patterns including various surface-mounted and interior magnet patterns,such as parallel magnetization pole pattern,Halbach arrays,spoke-type designs and their variants are summarized,and the characteristics of those magnet patterns in terms of flux-focusing effect,magnetic self-shielding effect,torque ripple,reluctance torque,magnet utilization ratio,and anti-demagnetization capability are compared.This paper can provide guidance and suggestion for the structure selection and design of PM brushless machines for high-performance industrial applications.
文摘Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.
文摘LEADSFON(XIAMEN)TEXTILE TECH CO.,LTD.is a manufacturer of knitting circular machines.Since 2002,the company has served as an ODM and supporting partner for the Italian brand"PILOTELLI".In 2014,LEADSFON officially acquired PILOTELLI,integrating advanced Italian technology into its core operations.
基金supported by the National Natural Science Foundation of China(No.52175414)the Natural Science Foundation of Jiangsu Province of China(No.BK20220134)+1 种基金the Fundamental Research Funds for the Central Universities,China(No.NE2023002)the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(No.KYCX24_0559)。
文摘During electrochemical machining(ECM),the passivation film formed on the surface of titanium alloy can lead to uneven dissolution and pitting.Solid particle erosion can effectively remove this passivation film.In this paper,the electrochemical dissolution behavior of Ti-6.5Al-2Zr-1Mo-1V(TA15)titanium alloy at without particle impact,low(15°)and high(90°)angle particle impact was investigated,and the influence of Al_(2)O_(3)particles on ECM was systematically expounded.It was found that under the condition of no particle erosion,the surface of electrochemically processed titanium alloy had serious pitting corrosion due to the influence of the passivation film,and the surface roughness(Sa)of the local area reached 10.088μm.Under the condition of a high-impact angle(90°),due to the existence of strain hardening and particle embedding,only the edge of the surface is dissolved,while the central area is almost insoluble,with the surface roughness(S_(a))reaching 16.086μm.On the contrary,under the condition of a low-impact angle(15°),the machining efficiency and surface quality of the material were significantly improved due to the ploughing effect and galvanic corrosion,and the surface roughness(S_(a))reached 2.823μm.Based on these findings,the electrochemical dissolution model of TA15 titanium alloy under different particle erosion conditions was established.
基金the National Key Research and Development Program of China(2021YFC2900300)the Natural Science Foundation of Guangdong Province(2024A1515030216)+2 种基金MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(GPMR202437)the Guangdong Province Introduced of Innovative R&D Team(2021ZT09H399)the Third Xinjiang Scientific Expedition Program(2022xjkk1301).
文摘The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.
基金supported by the SP2024/089 Project by the Faculty of Materials Science and Technology,VˇSB-Technical University of Ostrava.
文摘In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.
文摘The significance of precise energy usage forecasts has been highlighted by the increasing need for sustainability and energy efficiency across a range of industries.In order to improve the precision and openness of energy consumption projections,this study investigates the combination of machine learning(ML)methods with Shapley additive explanations(SHAP)values.The study evaluates three distinct models:the first is a Linear Regressor,the second is a Support Vector Regressor,and the third is a Decision Tree Regressor,which was scaled up to a Random Forest Regressor/Additions made were the third one which was Regressor which was extended to a Random Forest Regressor.These models were deployed with the use of Shareable,Plot-interpretable Explainable Artificial Intelligence techniques,to improve trust in the AI.The findings suggest that our developedmodels are superior to the conventional models discussed in prior studies;with high Mean Absolute Error(MAE)and Root Mean Squared Error(RMSE)values being close to perfection.In detail,the Random Forest Regressor shows the MAE of 0.001 for predicting the house prices whereas the SVR gives 0.21 of MAE and 0.24 RMSE.Such outcomes reflect the possibility of optimizing the use of the promoted advanced AI models with the use of Explainable AI for more accurate prediction of energy consumption and at the same time for the models’decision-making procedures’explanation.In addition to increasing prediction accuracy,this strategy gives stakeholders comprehensible insights,which facilitates improved decision-making and fosters confidence in AI-powered energy solutions.The outcomes show how well ML and SHAP work together to enhance prediction performance and guarantee transparency in energy usage projections.
基金Supported by the Shandong Province Traditional Chinese Medicine Technology Project,No.Q-2023147the Weifang Health Commission Research Project,No.WFWSJK-2023-033+3 种基金the Weifang City Science and Technology Development Plan(Medical Category),No.2023YX057the Weifang Medical University 2022 Campus Level Education and Teaching Reform and Research Project,No.2022YB051Norman Bethune Public Welfare Foundation,No.ezmr2023-037Special Research Project on Optimized Management of Acute Pain,Wu Jieping Medical Foundation.
文摘BACKGROUND Delayed wound healing is a common clinical complication following gastric cancer radical surgery,adversely affecting patient prognosis.With advances in artificial intelligence,machine learning offers a promising approach for developing predictive models that can identify high-risk patients and support early clinical intervention.AIM To construct machine learning-based risk prediction models for delayed wound healing after gastric cancer surgery to support clinical decision-making.METHODS We reviewed a total of 514 patients who underwent gastric cancer radical surgery under general anesthesia from January 1,2014 to December 30,2023.Seventy percent of the dataset was selected as the training set and 30%as the validation set.Decision trees,support vector machines,and logistic regression were used to construct a risk prediction model.The performance of the model was evaluated using accuracy,recall,precision,F1 index,and area under the receiver operating characteristic curve and decision curve.RESULTS This study included five variables:Sex,elderly,duration of abdominal drainage,preoperative white blood cell(WBC)count,and absolute value of neutrophils.These variables were selected based on their clinical relevance and statistical significance in predicting delayed wound healing.The results showed that the decision tree model outperformed the logistic regression and support vector machine models in both the training and validation sets.Specifically,the decision tree model achieved higher accuracy,F1 index,recall,and area under the curve(AUC)values.The support vector machine model also demonstrated better performance than logistic regression,with higher accuracy,recall,and F1 index,but a slightly lower AUC.The key variables of sex,elderly,duration of abdominal drainage,preoperative WBC count,and absolute value of neutrophils were found to be strong predictors of delayed wound healing.Patients with longer duration of abdominal drainage had a significantly higher risk of delayed wound healing,with a risk ratio of 1.579 compared to those with shorter duration of abdominal drainage.Similarly,preoperative WBC count,sex,elderly,and absolute value of neutrophils were associated with a higher risk of delayed wound healing,highlighting the importance of these variables in the model.CONCLUSION The model is able to identify high-risk patients based on sex,elderly,duration of abdominal drainage,preoperative WBC count,and absolute value of neutrophils can provide valuable insights for clinical decision-making.
文摘BACKGROUND Liver transplantation(LT)is the only curative,life-saving option for children and adults with end-stage liver disease.Due to the well-known shortage and heterogeneity of grafts,split LT(SLT)is an attractive strategy to expand the donor pool and reduce waitlist times.Given increased risk of cold ischemia time with SLT,machine perfusion represents a promising option to reduce it and optimize transplant logistics and outcomes.The present communication describes various possible combinations of procurement steps to perform SLT facilitated by placing one or both grafts on a normothermic machine perfusion(NMP)closed circuit device.CASE SUMMARY A 19-month-old female with biliary atresia after failed Kasai portoenterostomy and a 42-year-old woman with unresectable intrahepatic cholangiocarcinoma were selected as recipients for a SLT from a 17-year-old male donor.The SLT generated a left lateral segment and a right trisectional graft of appropriate volume for both recipients.After a mixed in-situ and ex-situ split,in order to improve logistics,the right trisectional graft was placed on a closed circuit NMP device,following an appropriate vascular reconstruction.Both grafts were implanted with excellent short-term outcomes.CONCLUSION Use of NMP with SLT for preservation prior to implantation allows not only for graft optimization but also for significant improvement of transplant logistics.We propose various models and standardization of logistic options for combining SLT with NMP to optimize graft availability and outcomes.
文摘BACKGROUND Severe dengue children with critical complications have been attributed to high mortality rates,varying from approximately 1%to over 20%.To date,there is a lack of data on machine-learning-based algorithms for predicting the risk of inhospital mortality in children with dengue shock syndrome(DSS).AIM To develop machine-learning models to estimate the risk of death in hospitalized children with DSS.METHODS This single-center retrospective study was conducted at tertiary Children’s Hospital No.2 in Viet Nam,between 2013 and 2022.The primary outcome was the in-hospital mortality rate in children with DSS admitted to the pediatric intensive care unit(PICU).Nine significant features were predetermined for further analysis using machine learning models.An oversampling method was used to enhance the model performance.Supervised models,including logistic regression,Naïve Bayes,Random Forest(RF),K-nearest neighbors,Decision Tree and Extreme Gradient Boosting(XGBoost),were employed to develop predictive models.The Shapley Additive Explanation was used to determine the degree of contribution of the features.RESULTS In total,1278 PICU-admitted children with complete data were included in the analysis.The median patient age was 8.1 years(interquartile range:5.4-10.7).Thirty-nine patients(3%)died.The RF and XGboost models demonstrated the highest performance.The Shapley Addictive Explanations model revealed that the most important predictive features included younger age,female patients,presence of underlying diseases,severe transaminitis,severe bleeding,low platelet counts requiring platelet transfusion,elevated levels of international normalized ratio,blood lactate and serum creatinine,large volume of resuscitation fluid and a high vasoactive inotropic score(>30).CONCLUSION We developed robust machine learning-based models to estimate the risk of death in hospitalized children with DSS.The study findings are applicable to the design of management schemes to enhance survival outcomes of patients with DSS.
文摘The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource demands.Automated Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification tasks.This paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these limitations.Our method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature groups.The Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization problem.Empirical validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.
文摘The prediction of tool wear in CNC machine tools is a critical aspect of ensuring the efficient operation and longevity of manufacturing equipment.Tool wear significantly impacts machining accuracy,surface finish quality,and operational downtime,making its prediction essential for proactive maintenance strategies.This paper explores the integration of Digital Twin technology with tool wear prediction models to enhance the precision and reliability of wear forecasting in CNC machines.We review existing methodologies for tool wear prediction,including physics-based models,data-driven approaches,and hybrid models,with an emphasis on their strengths and limitations.Furthermore,the paper highlights the role of Digital Twin technology in creating real-time,virtual replicas of CNC machines that can dynamically monitor tool wear and provide actionable insights for optimization.By leveraging real-time data and advanced simulation techniques,Digital Twin-based prediction models offer significant improvements over traditional methods.The paper concludes by discussing future directions for integrating machine learning,deep learning,and real-time data analytics into the tool wear prediction process,ultimately contributing to the development of more intelligent and adaptive manufacturing systems.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFA1402304)the National Natural Science Foundation of China(Grant Nos.12034009,12374005,52288102,52090024,and T2225013)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Program for JLU Science and Technology Innovative Research Team.
文摘Crystal structure prediction(CSP)is a foundational computational technique for determining the atomic arrangements of crystalline materials,especially under high-pressure conditions.While CSP plays a critical role in materials science,traditional approaches often encounter significant challenges related to computational efficiency and scalability,particularly when applied to complex systems.Recent advances in machine learning(ML)have shown tremendous promise in addressing these limitations,enabling the rapid and accurate prediction of crystal structures across a wide range of chemical compositions and external conditions.This review provides a concise overview of recent progress in ML-assisted CSP methodologies,with a particular focus on machine learning potentials and generative models.By critically analyzing these advances,we highlight the transformative impact of ML in accelerating materials discovery,enhancing computational efficiency,and broadening the applicability of CSP.Additionally,we discuss emerging opportunities and challenges in this rapidly evolving field.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Groups Program Grant No.(RGP-1444-0057).
文摘Dementia is a neurological disorder that affects the brain and its functioning,and women experience its effects more than men do.Preventive care often requires non-invasive and rapid tests,yet conventional diagnostic techniques are time-consuming and invasive.One of the most effective ways to diagnose dementia is by analyzing a patient’s speech,which is cheap and does not require surgery.This research aims to determine the effectiveness of deep learning(DL)and machine learning(ML)structures in diagnosing dementia based on women’s speech patterns.The study analyzes data drawn from the Pitt Corpus,which contains 298 dementia files and 238 control files from the Dementia Bank database.Deep learning models and SVM classifiers were used to analyze the available audio samples in the dataset.Our methodology used two methods:a DL-ML model and a single DL model for the classification of diabetics and a single DL model.The deep learning model achieved an astronomic level of accuracy of 99.99%with an F1 score of 0.9998,Precision of 0.9997,and recall of 0.9998.The proposed DL-ML fusion model was equally impressive,with an accuracy of 99.99%,F1 score of 0.9995,Precision of 0.9998,and recall of 0.9997.Also,the study reveals how to apply deep learning and machine learning models for dementia detection from speech with high accuracy and low computational complexity.This research work,therefore,concludes by showing the possibility of using speech-based dementia detection as a possibly helpful early diagnosis mode.For even further enhanced model performance and better generalization,future studies may explore real-time applications and the inclusion of other components of speech.
基金supported by JSPS KAKENHI Grant Number JP21K14064 and JP23K13239.
文摘Maintaining high groundwater level(GWL)is important for preventing fires in peatlands.This study proposes GWL prediction using machine learning methods for forest plantations in Indonesian tropical peatlands.Deep neural networks(DNN)have been used for prediction;however,they have not been applied to groundwater prediction in Indonesian peatlands.Tropical peatland is characterized by high permeability and forest plantations are surrounded by several canals.By predicting daily differences in GWL,the GWL can be predicted with high accuracy.DNNs,random forests,support vector regression,and XGBoost were compared,all of which indicated similar errors.The SHAP value revealed that the precipitation falling on the hill rapidly seeps into the soil and flows into the canals,which agrees with the fact that the soil has high permeability.These findings can potentially be used to alleviate and manage future fires in peatlands.
