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Clinically adaptable machine learning model to identify early appreciable features of diabetes

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Intelligent Medicine 2024年第1期4卷 22-32页

作者： Nurjahan Nipa Mahmudul Hasan Riyad Shahriare Satu Walliullah Koushik Chandra Howlader Mohammad Ali Moni department of information and Communication Technology Bangabandhu Sheikh Mujibur Rahman Digital UniversityBangladeshKaliakairGazipur1750 Bangladesh department of Applied Mathematics Noakhali Science and Technology UniversityNoakhali3814Bangladesh department of Management information Systems Noakhali Science and Technology UniversityNoakhali3814Bangladesh department of Computer Science and Telecommunication Engineering Noakhali Science and Technology UniversityNoakhali3814Bangladesh department of Computer Science North Dakota State UniversityFargo58105NDUnited States School of health and Rehabilitation Sciences Faculty of Health and Behavioural SciencesThe University of QueenslandSt LuciaQLD 4072Australia

Objective Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood *** disease happens for a number of reasons including modern lifestyle,lethargic attitude,unhealthy food consumption,family history,age,overweight,*** aim of this study was to propose a machine learning based prediction model that detected diabetes at the *** In this work,we collected 520 patients records from the University of California,Irvine(UCI)machine learning repository of Sylhet Diabetes Hospital,***,a similar questionnaire of that hospital was followed and assembled 558 patients records from all over Bangladesh through this ***,we accumulated patient records of these two *** the next step,these datasets were cleaned and applied thirty five state-of-arts classifiers such as logistic regression(LR),K nearest neighbors(KNN),support vector classifier(SVC),Nave Byes(NB),decision tree(DT),random forest(RF),stochastic gradient descent(SGD),Perceptron,AdaBoost,XGBoost,passive aggressive classifier(PAC),ridge classifier(RC),Nu-support vector classifier(NuSVC),linear support vector classifier(LSVC),calibrated classifier CV(CCCV),nearest centroid(NC),Gaussian process classifier(GPC),multinomial NB(MNB),complement NB,Bernoulli NB(BNB),categorical NB,Bagging,extra tree(ET),gradiant boosting classifier(GBC),Hist gradiant boosting classifier(HGBC),one vs rest classifier(OVsRC),multi-layer perceptron(MLP),label propagation(LP),label spreading(LS),stacking,ridge classifier CV(RCCV),logistic regression CV(LRCV),linear discriminant analysis(LDA),quadratic discriminant analysis(QDA),and light gradient boosting machine(LGBM)to explore best stable predictive *** performance of the classifiers has been measured using five metrics such as accuracy,precision,recall,F1-score,and area under the receiver operating ***,these outcomes were interpret

关键词： Diabetes Early features Machine learning Classification

Adversarial data splitting for domain generalization

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Science China(information Sciences) 2024年第5期67卷 28-42页

作者： Xiang GU Jian SUN Zongben XU School of Mathematics and statistics Xi'an Jiaotong University

Domain generalization aims to learn a model that is generalizable to an unseen target domain,which is a fundamental and challenging task in machine learning for out-of-distribution generalization. This paper proposes a novel domain generalization approach that enforces the learned model to be able to generalize well over the train/val subset splitting of the training dataset. This idea is modeled herein as an adversarial data splitting framework, formulated as a min-max optimization problem inspired by the metalearning approach. The min-max optimization problem is solved by iteratively splitting the training dataset into the training and val subsets to maximize the domain shift measured by the objective function and updating the model parameters to enable the model to generalize well from the training subset to the val subset by minimizing the objective function. This adversarial training approach does not assume the known domain labels of the training data; instead, it automatically investigates the “hard” splitting of the train/val subsets to learn the generalizable model. Extensive experimental results using three benchmark datasets demonstrate the superiority of this approach. In addition, we derive a generalization error bound for the theoretical understanding of our proposed approach.

关键词： domain generalization adversarial learning data splitting meta-learning out of distribution

Semi-analytical investigation of heat transfer in a porous convective radiative moving longitudinal fin exposed to magnetic field in the presence of a shape-dependent trihybrid nanofluid

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Applied Mathematics and Mechanics(English Edition) 2024年第1期45卷 197-216页

作者： C.G.PAVITHRA B.J.GIREESHA M.L.KEERTHI department of PG Studies and Research in Mathematics Kuvempu UniversityShankaraghatta 577451India department of Mathematics and statistics M.S.Ramaiah University of Applied SciencesBengaluru 560058India

The thermal examination of a non-integer-ordered mobile fin with a magnetism in the presence of a trihybrid nanofluid(Fe_3O_4-Au-Zn-blood) is carried out. Three types of nanoparticles, each having a different shape, are considered. These shapes include spherical(Fe_3O_4), cylindrical(Au), and platelet(Zn) configurations. The combination approach is utilized to evaluate the physical and thermal characteristics of the trihybrid and hybrid nanofluids, excluding the thermal conductivity and dynamic viscosity. These two properties are inferred by means of the interpolation method based on the volume fraction of nanoparticles. The governing equation is transformed into a dimensionless form, and the Adomian decomposition Sumudu transform method(ADSTM) is adopted to solve the conundrum of a moving fin immersed in a trihybrid nanofluid. The obtained results agree well with those numerical simulation results, indicating that this research is reliable. The influence of diverse factors on the thermal overview for varying noninteger values of γ is analyzed and presented in graphical representations. Furthermore, the fluctuations in the heat transfer concerning the pertinent parameters are studied. The results show that the heat flux in the presence of the combination of spherical, cylindrical, and platelet nanoparticles is higher than that in the presence of the combination of only spherical and cylindrical nanoparticles. The temperature at the fin tip increases by 0.705 759% when the value of the Peclet number increases by 400%, while decreases by 11.825 13% when the value of the Hartman number increases by 400%.

关键词： convection radiation moving longitudinal fin Adomian decomposition Sumudu transform method(ADSTM) trihybrid nanofluid magnetic field

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Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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Computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan department of information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan department of information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia department of Management information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan department of Electrical Engineering Balochistan University of information Technology Engineeringand Management SciencesQuetta87300Pakistan department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

Associations between parental adherence to healthy lifestyles and cognitive performance in offspring:A prospective cohort study in China

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Chinese Medical Journal 2024年第6期137卷 683-693页

作者： Rongxia Lv Yuhui Huang Siyi Huang Shiyi Wu Siwen Wang Guangyu Hu Yanan Ma Peige Song Jorge E.Chavarro S.V.Subramanian Chunling Lu Zhihui Li Changzheng Yuan School of Public health the Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang 310058China department of Nutrition Harvard T.H.Chan School of Public HealthBostonMA 02115USA Institute of Medical information/Center for health Policy and Management Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730China department of Biostatistics and Epidemiology School of Public HealthChina Medical UniversityShenyangLiaoning 110122China department of Social and Behavioral Sciences Harvard T.H.Chan School of Public HealthBostonMA 02115USA Division of Global health Equity Brigham and Women’s HospitalHarvard Medical SchoolBostonMA 02115USA department of Global health and Social Medicine Harvard Medical SchoolBostonMA 02115USA Vanke School of Public health Tsinghua UniversityBeijing 100084China department of Global health and Population Harvard T.H.Chan School of Public HealthBostonMA 02115USA

Background:Previous studies have reported associations of specific maternal and paternal lifestyle factors with offspring’s cognitive development during early *** study aimed to investigate the prospective associations between overall parental lifestyle and offspring’s cognitive performance during adolescence and young adulthood in ***:We included 2531 adolescents aged 10-15 years at baseline in 2010 from the China Family Panel Studies.A healthy parental lifestyle score(ranged 0-5)was constructed based on the following five modifiable lifestyle factors:Smoking,drinking,exercise,sleep,and *** estimating equation models were used to examine the association between baseline parental healthy lifestyle scores and offspring’s fluid and crystallized intelligence in subsequent years(2012,2014,2016,and 2018).Results:Offspring in the top tertile of parental healthy lifestyle scores performed better in overall fluid intelligence(multivariable-adjusted β=0.53,95%confidence interval[CI]:0.29-0.77)and overall crystallized intelligence(multivariable-adjusted β=0.35,95%CI:0.16-0.54)than those in the bottom tertile of parental healthy lifestyle *** results were similar after further adjustment for the offspring’s healthy lifestyle scores and persisted across the subgroups of parental socioeconomic ***,maternal and paternal healthy lifestyle scores were independently associated with better offspring’s cognitive performance,with significant contribution observed for paternal never-smoking,weekly exercise,and diversified *** both parents and offspring adhered to a healthier lifestyle,we observed the highest level of the offspring’s overall crystallized ***:Our study indicates that parental adherence to a healthier lifestyle is associated with significantly better offspring’s cognitive performance during adolescence and early adulthood,regardless of socioeconomic *** findings highlight the poten

关键词： Cohort study Parental healthy lifestyle Offspring Cognitive performance Intelligence Adolescence

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Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

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Computers, Materials & Continua 2024年第4期79卷 675-694页

作者： Hala AlShamlan Halah AlMazrua department of information Technology College of Computer and Information SciencesKing Saud UniversityP.O.Box 145111Riyadh4545Saudi Arabia

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature *** for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization *** aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer *** selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray *** proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is *** hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of *** outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment *** proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer *** improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification *** harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

关键词： Bio-inspired algorithms bioinformatics cancer classification evolutionary algorithm feature selection gene expression grey wolf optimizer harris hawks optimization k-nearest neighbor support vector machine

Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3101-3123页

作者： Mohammad Mehedi Hassan Salman A.AlQahtani Mabrook S.AlRakhami Ahmed Zohier Elhendi department of information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Science Technology and Innovation department King Saud UniversityRiyadh11543Saudi Arabia

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： Explainable AI COVID-19 CT images deep learning

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

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Computers, Materials & Continua 2024年第4期79卷 1157-1185页

作者： Sapiah Sakri Shakila Basheer Zuhaira Muhammad Zain Nurul Halimatul Asmak Ismail Dua’Abdellatef Nassar Manal Abdullah Alohali Mais Ayman Alharaki department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia department of Computer Science and information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications *** detection is crucial as sepsis can rapidly escalate if left *** in deep learning(DL)offer powerful tools to address this ***:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis *** the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier ***:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of *** time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis *** models were trained usingthe Medical information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical *** confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate ***:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis *** results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one ***:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

关键词： Temporal derivatives hybrid deep learning predicting sepsis onset MIMIC III machine learning(ML) deep learning

Semi-derived Ringel-Hall algebras and Hall algebras of odd-periodic relative derived categories

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Science China Mathematics 2024年第8期67卷 1735-1760页

作者： Ji Lin Liangang Peng department of Mathematics and statistics Fuyang Normal UniversityFuyang 236037China School of Mathematics Sichuan UniversityChengdu 610064China

Let t be a positive integer and A be a hereditary abelian category satisfying some finiteness *** define the semi-derived Ringel-Hall algebra of A from the category C_(Z/t)(A)of Z/t-graded complexes and obtain a natural basis of the semi-derived Ringel-Hall ***,we describe the semiderived Ringel-Hall algebra by the generators and defining *** particular,if t is an odd integer,we show an embedding of the derived Hall algebra of the odd-periodic relative derived category in the extended semi-derived Ringel-Hall algebra.

关键词： hereditary abelian categories the categories of Z/t-graded complexes semi-derived Ringel-Hall algebras derived Hall algebras

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