A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

Functional Confirmation Using a Medical X-Ray System of a Semiconductor Survey Meter

In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study determined if it is possible to use the X-ray system installed in each medical facility to calculate the time constant of a semiconductor survey meter and confirm the meter’s function. An additional filter was attached to the medical X-ray system to satisfy the standards of N-60 to N-120, more copper plates were added as needed, and the first and second half-value layers were calculated to enable comparisons of the facility’s X-ray system quality with the N-60 to N-120 quality values. Next, we used a medical X-ray system to measure the leakage dose and calculate the time constant of the survey meter. The functionality of the meter was then checked and compared with the energy characteristics of the meter. The experimental results showed that it was possible to use a medical X-ray system to reproduce the N-60 to N-120 radiation quality values and to calculate the time constant from the measured results, assuming actual leakage dosimetry for that radiation quality. We also found that the calibration factor was equivalent to that of the energy characteristics of the survey meter.

Machine Learning-Enabled Communication Approach for the Internet of Medical Things

The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed decisions.Generally,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing approaches.In this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT network.For this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable devices.Additionally,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties.Apart from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy traffic.Furthermore,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is available.The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks.Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,respectively.Moreover,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes.

面向机器阅读理解的医学域数据集MedicalQA

机器阅读理解旨在利用算法让计算机理解段落语义并回答用户提出的问题,该任务所用数据集的质量可直接影响模型的实验结果。为丰富机器阅读理解的医学领域数据集,本文以爬虫和人工标注的方式构建了面向机器阅读理解的医学域数据集MedicalQA。本数据集以寻医问药网和39健康网两大医疗平台为主要数据来源,包含19502个段落、问题和答案,内容涉及内科、外科、妇产科等9大科室。数据集形式为excel文件,由5列组成,第一列为段落ID,第二列为段落所属科室,第三列为段落内容,第四列为问题,第五列为问题对应答案。本数据集的构建,有利于机器阅读理解模型的鲁棒性研究以及医学问答系统的构建,也能促进机器阅读理解领域的医学数据集共享。

A machine learning approach for predictings stroke

Background:Stroke is one of the most dangerous and life-threatening disease as it can cause lasting brain damage,long-term disability,or even death.The early detection of warning signs of a stroke can help save the life of a patient.In this paper,we adopted machine learning approaches to predict strokes and identify the three most important factors that are associated with strokes.Methods:This study used an open-access stroke prediction dataset.We developed 11 machine learning models and compare the results to those found in prior studies.Results:The accuracy,recall and area under the curve for the random forest model in our study is significantly higher than those of other studies.Machine learning models,particularly the random forest algorithm,can accurately predict the risk of stroke and support medical decision making.Conclusion:Our findings can be applied to design clinical prediction systems at the point of care.

Medical Diagnosis Using Machine Learning:A Statistical Review

Decision making in case of medical diagnosis is a complicated process.A large number of overlapping structures and cases,and distractions,tiredness,and limitations with the human visual system can lead to inappropriate diagnosis.Machine learning(ML)methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis.Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published.Hence,to determine the use of ML to improve the diagnosis in varied medical disciplines,a systematic review is conducted in this study.To carry out the review,six different databases are selected.Inclusion and exclusion criteria are employed to limit the research.Further,the eligible articles are classied depending on publication year,authors,type of articles,research objective,inputs and outputs,problem and research gaps,and ndings and results.Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis.The ndings of this study show the most used ML methods and the most common diseases that are focused on by researchers.It also shows the increase in use of machine learning for disease diagnosis over the years.These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.

A Comprehensive Review on Medical Diagnosis Using Machine Learning

The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.

Transverse emittance measurement for the heavy ion medical machine cyclotron

The transverse emittance of the extracted beam from the heavy ion medical machine cyclotron is measured and then optimized for injection into the synchrotron.For the purposes of cross-validation,three methods,i.e.,slit-grid,Q-scan,and 3-grid,are used to measure the emittance.In the slit-grid technique,an automatic selection of the region of interest is adopted to isolate the major noise from the beam phase space,which is an improvement over the traditional technique.After iterating over the contour level,an unbiased measurement of the emittance can be obtained.An improvement in the thin lens technique is implemented in the Q-scan method.The results of these measurements are presented.

Review of intelligent diagnosis methods for imaging gland cancer based on machine learning

Gland cancer is a high-incidence disease that endangers human health,and its early detection and treatment require efficient,accurate,and objective intelligent diagnosis methods.In recent years,the advent of machine learning techniques has yielded satisfactory results in intelligent gland cancer diagnosis based on clinical images,significantly improving the accuracy and efficiency of medical image interpretation while reducing the workload of doctors.The focus of this study is to review,classify,and analyze intelligent diagnosis methods for imaging gland cancer based on machine learning and deep learning.This paper briefly introduces some basic imaging principles of multimodal medical images,such as the commonly used computed tomography(CT),magnetic resonance imaging(MRI),ultrasound(US),positron emission tomography(PET),and pathology.In addition,the intelligent diagnosis methods for imaging gland cancer were further classified into supervised learning and weakly supervised learning.Supervised learning consists of traditional machine learning methods,such as K-nearest neighbor algorithm(KNN),support vector machine(SVM),and multilayer perceptron,and deep learning methods evolving from convolutional neural network(CNN).By contrast,weakly supervised learning can be further categorized into active learning,semisupervised learning,and transfer learning.State-of-the-art methods are illustrated with implementation details,including image segmentation,feature extraction,and optimization of classifiers.Their performances are evaluated through indicators,such as accuracy,precision,and sensitivity.In conclusion,the challenges and development trends of intelligent diagnosis methods for imaging gland cancer were addressed and discussed.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Metal-Halide Perovskite Submicrometer-Thick Films for Ultra-Stable Self-Powered Direct X-Ray Detectors

Metal-halide perovskites are revolutionizing the world of X-ray detectors,due to the development of sensitive,fast,and cost-effective devices.Self-powered operation,ensuring portability and low power consumption,has also been recently demonstrated in both bulk materials and thin films.However,the signal stability and repeatability under continuous X-ray exposure has only been tested up to a few hours,often reporting degradation of the detection performance.Here it is shown that self-powered direct X-ray detectors,fabricated starting from a FAPbBr_(3)submicrometer-thick film deposition onto a mesoporous TiO_(2)scaffold,can withstand a 26-day uninterrupted X-ray exposure with negligible signal loss,demonstrating ultra-high operational stability and excellent repeatability.No structural modification is observed after irradiation with a total ionizing dose of almost 200 Gy,revealing an unexpectedly high radiation hardness for a metal-halide perovskite thin film.In addition,trap-assisted photoconductive gain enabled the device to achieve a record bulk sensitivity of 7.28 C Gy^(−1)cm^(−3)at 0 V,an unprecedented value in the field of thin-film-based photoconductors and photodiodes for“hard”X-rays.Finally,prototypal validation under the X-ray beam produced by a medical linear accelerator for cancer treatment is also introduced.

Feedback Control of Medication Delivery Device Using Machine Learning-Based Control Co-Design

Although the opioid crisis is a problem worldwide, recent emerging technology has the potential of curtailing the epidemic. By administering micro-doses of medication as needed, a feedback-driven medicine pump could lessen the highs and lows associated with the formation of an addiction. The focus of this study was to develop a feedback control loop for this pump that optimizes drug concentration in the bloodstream based on set criteria. In the process of optimization of the system, the mathematical model representing the system was analyzed to find an open loop transfer function. Using this function, a PID tuner was applied to set feedback control. Both machine learning (ML) and deep learning (DL) techniques are explored to act as a classifier that aids the pump in administering doses. The setpoint concentration of medication in the patient’s bloodstream was calculated to be 7.55 mg/ml this setpoint was the basis for steady state concentration of the transfer function. When a PID tuner was added to the feedback system, the plot was optimized to satisfy the design criteria of a rise time less than 25-minutes and no more than a 5% overshoot of the setpoint concentration. Naïve Bayesian (NB), Tree and support-vector machines (SVM) classifiers achieved the best classification accuracy of 100%. A DL network was successfully developed to predict patient class. This work is the theoretical basis for developing a feedback-driven medicine pump and an algorithm that can classify patients based on their body’s metabolism that will aid the doctor in formatting the medicine pump so that the patient is receiving the proper amount of medication.

Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

Effective medical diagnosis is dramatically expensive,especially in third-world countries.One of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant challenge.Hence,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks.Unfortunately,experimentation and utilization of different models of transfer learning do not achieve satisfactory results.In this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related diseases.This model is based on classification,denoising autoencoder,and transfer learning.Firstly,pre-processing is employed to prepare the medical images.It depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis process.Then,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect pneumonia.The proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray images.Finally,a comparative study is introduced for the classification performance with and without the denoising process.The proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and testing.To demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN models.The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the existing diagnosis models.

Advances of Artificial Intelligence Application in Medical Imaging of Ovarian Cancers

Ovarian cancer is one of the three most common gynecological cancers in the world,and is regarded as a priority in terms of women’s cancer.In the past few years,many researchers have attempted to develop and apply artificial intelligence(AI)techniques to multiple clinical scenarios of ovarian cancer,especially in the field of medical imaging.AI-assisted imaging studies have involved computer tomography(CT),ultrasonography(US),and magnetic resonance imaging(MRI).In this review,we perform a literature search on the published studies that using AI techniques in the medical care of ovarian cancer,and bring up the advances in terms of four clinical aspects,including medical diagnosis,pathological classification,targeted biopsy guidance,and prognosis prediction.Meanwhile,current status and existing issues of the researches on AI application in ovarian cancer are discussed.

An Ensemble Methods for Medical Insurance Costs Prediction Task

The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs.The open dataset is used for data analysis methods development.The usage of artificial intelligence in the management of financial risks will facilitate economic wear time and money and protect patients’health.Machine learning is associated withmany expectations,but its quality is determined by choosing a good algorithm and the proper steps to plan,develop,and implement the model.The paper aims to develop three new ensembles for individual insurance costs prediction to provide high prediction accuracy.Pierson coefficient and Boruta algorithm are used for feature selection.The boosting,stacking,and bagging ensembles are built.A comparison with existing machine learning algorithms is given.Boosting modes based on regression tree and stochastic gradient descent is built.Bagged CART and Random Forest algorithms are proposed.The boosting and stacking ensembles shown better accuracy than bagging.The tuning parameters for boosting do not allow to decrease the RMSE too.So,bagging shows its weakness in generalizing the prediction.The stacking is developed using K Nearest Neighbors(KNN),Support Vector Machine(SVM),Regression Tree,Linear Regression,Stochastic Gradient Boosting.The random forest(RF)algorithm is used to combine the predictions.One hundred trees are built forRF.RootMean Square Error(RMSE)has lifted the to 3173.213 in comparison with other predictors.The quality of the developed ensemble for RootMean Squared Error metric is 1.47 better than for the best weak predictor(SVR).

A review of medical artificial intelligence

Since the concept of“artificial intelligence”was introduced in 1956,it has led to numerous technological innovations in human medicine and completely changed the traditional model of medicine.In this study,we mainly explain the application of artificial intelligence in various fields of medicine from four aspects:machine learning,intelligent robot,image recognition technology,and expert system.In addition,we discuss the existing problems and future trends in these areas.In recent years,through the development of globalization,various research institutions around the world has conducted a number of researches on this subject.Therefore,medical artificial intelligence has attained significant breakthroughs and will demonstrate wide development prospection in the future.

Using AdaBoost Meta-Learning Algorithm for Medical News Multi-Document Summarization

Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document summarization that differs from the single one in which the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Since the number and variety of online medical news make them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summarization can be useful for easy study of information on the web. Hence we propose a new approach based on machine learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sentences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different medical websites. Then we compare our results with some existing approaches.

A Collaborative Medical Diagnosis System Without Sharing Patient Data

As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploiting its potential because the data usually sits in data silos,and privacy and security regulations restrict their access and use.To address these issues,we built a secured and explainable machine learning framework,called explainable federated XGBoost(EXPERTS),which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data.It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals.To study the performance,we evaluate our approach by real-world datasets,and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.

Fuzzy Logic with Archimedes Optimization Based Biomedical Data Classification Model

Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making.Besides,the advances of machine learning(ML)techniques assist to perform the effective classification task.With this motivation,this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm(FCA-BFS)with optimal support vector machine(OSVM)model,named FCABFS-OSVM for medical data classification.The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models.Besides,the proposed FCABFSOSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification performance.Moreover,the OSVM model investigates the clustered medical data to perform classification process.Furthermore,Archimedes optimization algorithm(AOA)is utilized to the SVM parameters and boost the medical data classification results.A wide range of simulations takes place to highlight the promising performance of the FCABFS-OSVM technique.Extensive comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.